Research-paper recommender systems: a literature survey

Abstract

In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users’ information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. 1.

    Some recommender systems also recommended “citations” but in our opinion, differences between recommending papers and citations are marginal, which is why we do not distinguish between these two terms in this paper.

  2. 2.

    http://citeseerx.ist.psu.edu.

  3. 3.

    http://scholar.google.com/scholar?sciupd=1&hl=en&as_sdt=0,5.

  4. 4.

    http://www.ncbi.nlm.nih.gov/pubmed.

  5. 5.

    http://www.researchgate.net/.

  6. 6.

    http://www.citeulike.org/.

  7. 7.

    http://www.docear.org.

  8. 8.

    http://www.mendeley.com/.

  9. 9.

    http://www.bibtip.com/.

  10. 10.

    http://www.exlibrisgroup.com/category/bXUsageBasedServices.

  11. 11.

    http://refseer.ist.psu.edu/.

  12. 12.

    http://theadvisor.osu.edu/.

  13. 13.

    http://lab.cisti-icist.nrc-cnrc.gc.ca/Sarkanto/.

  14. 14.

    http://www.um.org/conferences.

  15. 15.

    http://recsys.acm.org/.

  16. 16.

    Recommendation frameworks such as LensKit or Mahout may also be helpful for researchers and developers, but frameworks are not the topic of this paper.

  17. 17.

    http://grouplens.org/datasets/movielens/.

  18. 18.

    http://labrosa.ee.columbia.edu/millionsong/.

  19. 19.

    http://www.kde.cs.uni-kassel.de/ws/dc13/.

  20. 20.

    http://trec.nist.gov/data.html.

  21. 21.

    http://www.citeulike.org/faq/data.adp.

  22. 22.

    https://www.kde.cs.uni-kassel.de/bibsonomy/dumps/.

  23. 23.

    http://csxstatic.ist.psu.edu/about/data.

  24. 24.

    http://www.comp.nus.edu.sg/~sugiyama/SchPaperRecData.html.

  25. 25.

    http://core.kmi.open.ac.uk/intro/data_dumps.

  26. 26.

    Relatedness between genes was retrieved from an external data source that maintained information about gene relatedness.

  27. 27.

    Attribute similarity was calculated, e.g., based on the number of pages.

  28. 28.

    http://grouplens.org/.

  29. 29.

    The recommender systems of Mendeley, CiteULike, and CiteSeer are counted twice because they offer or offered two independent recommender systems.

  30. 30.

    We classified a recommender system as not actively maintained if no article was published or no changes were made to the system for a year.

  31. 31.

    ResearchGate also applied other recommender systems, e.g., for people or news, and it seems that these approaches are more sophisticated.

  32. 32.

    Median author count was three, maximum count eleven.

  33. 33.

    http://www.mymedialite.net/.

  34. 34.

    http://lenskit.grouplens.org/.

  35. 35.

    http://mahout.apache.org/.

  36. 36.

    http://www.duineframework.org/.

  37. 37.

    http://code.richrelevance.com/reclab-core/.

  38. 38.

    http://easyrec.org/.

  39. 39.

    http://ls13-www.cs.uni-dortmund.de/homepage/recommender101/index.shtml.

References

  1. 1.

    Bollacker, K.D., Lawrence, S., Giles, C.L.: CiteSeer: an autonomous web agent for automatic retrieval and identification of interesting publications. In: Proceedings of the 2nd international conference on Autonomous agents, pp. 116–123 (1998)

  2. 2.

    Google Scholar, Scholar Update: Making New Connections, Google Scholar Blog. http://googlescholar.blogspot.de/2012/08/scholar-updates-making-new-connections.html

  3. 3.

    Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P., Jaakkola, T.: Mixed membership stochastic block models for relational data with application to protein–protein interactions. In: Proceedings of the International Biometrics Society Annual Meeting, pp. 1–34 (2006)

  4. 4.

    Arnold, A., Cohen, W.W.: Information extraction as link prediction: using curated citation networks to improve gene detection. In: Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications, pp. 541–550 (2009)

  5. 5.

    Beel, J., Langer, S., Genzmehr, M.: Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling. In: Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), pp. 395–399 (2013)

  6. 6.

    Beel, J., Langer, S., Genzmehr, M., Nürnberger, A.: Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times. In: Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), vol. 8092, pp. 390–394 (2013)

  7. 7.

    Beel, J., Langer, S., Genzmehr, M., Nürnberger, A.: Introducing Docear’s Research Paper Recommender System. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’13), pp. 459–460 (2013)

  8. 8.

    Beel, J., Langer, S., Nürnberger, A., Genzmehr, M.: The Impact of Demographics (Age and Gender) and Other User Characteristics on Evaluating Recommender Systems. In: Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), pp. 400–404 (2013)

  9. 9.

    Böhm, W., Geyer-schulz, A., Hahsler, M., Jahn, M.: Repeat-Buying Theory and Its Application for Recommender Services. In: Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., pp. 229–239 (2003)

  10. 10.

    Baez, M., Mirylenka, D., Parra, C.: Understanding and supporting search for scholarly knowledge. In: Proceeding of the 7th European Computer Science Summit, pp. 1–8 (2011)

  11. 11.

    Beel, J., Gipp, B., Langer, S., Genzmehr, M.: Docear: an academic literature suite for searching, organizing and creating academic literature. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 465–466 (2011)

  12. 12.

    Beel, J., Gipp, B., Mueller, C.: SciPlore MindMapping’—a tool for creating mind maps combined with PDF and reference management. D-Lib Mag. 15(11) (2009)

  13. 13.

    Bethard, S., Jurafsky, D.: Who should I cite: learning literature search models from citation behavior. In: Proceedings of the 19th ACM international conference on Information and knowledge management, pp. 609–618 (2010)

  14. 14.

    Bogers, T., van den Bosch, A.: Recommending scientific articles using citeulike. In: Proceedings of the 2008 ACM conference on Recommender systems, pp. 287–290 (2008)

  15. 15.

    Bollen, J., Van de Sompel, H.: An architecture for the aggregation and analysis of scholarly usage data. In: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, pp. 298–307 (2006)

  16. 16.

    CiteSeerX, T.: About RefSeer. http://refseer.ist.psu.edu/about (2012)

  17. 17.

    CiteULike: My Top Recommendations. Website http://www.citeulike.org/profile/username/recommendations (2011)

  18. 18.

    CiteULike: Science papers that interest you. Blog. http://blog.citeulike.org/?p=11 (2009)

  19. 19.

    CiteULike: Data from CiteULike’s new article recommender. Blog, http://blog.citeulike.org/?p=136 (2009)

  20. 20.

    Caragea, C., Silvescu, A., Mitra, P., Giles, C.L.: Can’t See the Forest for the Trees? A Citation Recommendation System. In: iConference 2013 Proceedings, pp. 849–851 (2013)

  21. 21.

    Chandrasekaran, K., Gauch, S., Lakkaraju, P., Luong, H.: Concept-based document recommendations for citeseer authors. In: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 83–92 (2008)

  22. 22.

    Choochaiwattana, W.: Usage of tagging for research paper recommendation. In: Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), vol. 2, pp. 439–442 (2010)

  23. 23.

    Councill, I., Giles, C., Di Iorio, E., Gori, M., Maggini, M., Pucci, A.: Towards next generation CiteSeer: a flexible architecture for digital library deployment. In: Research and Advanced Technology for Digital Libraries, pp. 111–122 (2006)

  24. 24.

    Dong, R., Tokarchuk, L., Ma, A.: Digging Friendship: Paper Recommendation in Social Network. In: Proceedings of Networking and Electronic Commerce Research Conference (NAEC 2009), pp. 21–28 (2009)

  25. 25.

    ExLibris: bX Usage-Based Services transform your discovery experience!, Web page, http://www.exlibrisgroup.com/category/bXUsageBasedServices (2013)

  26. 26.

    Ekstrand, M.D., Kannan, P., Stemper, J.A., Butler, J.T., Konstan, J.A., Riedl, J.T.: Automatically building research reading lists. In: Proceedings of the 4th ACM conference on Recommender systems, pp. 159–166 (2010)

  27. 27.

    Erosheva, E., Fienberg, S., Lafferty, J.: Mixed-membership models of scientific publications. Proc. Natl. Acad. Sci. U. S. Am. 101(Suppl 1), 5220–5227 (2004)

    Article  Google Scholar 

  28. 28.

    Franke, M., Geyer-Schulz, A.: Using restricted random walks for library recommendations and knowledge space exploration. Int. J. Pattern Recognit. Artif. Intell. 21(02), 355–373 (2007)

  29. 29.

    Ferrara, F., Pudota, N., Tasso, C.: A Keyphrase-Based Paper Recommender System. In: Proceedings of the IRCDL’11, pp. 14–25 (2011)

  30. 30.

    Geyer-Schulz, A., Hahsler, M.: Comparing two recommender algorithms with the help of recommendations by peers. In: Proceedings of the WEBKDD 2002—Mining Web Data for Discovering Usage Patterns and Profiles, pp. 137–158 (2003)

  31. 31.

    Geyer-Schulz, A., Hahsler, M.: Evaluation of recommender algorithms for an internet information broker based on simple association rules and on the repeat-buying theory. In: Proceedings of the 4th WebKDD Workshop: Web Mining for Usage Patterns and User Profiles, pp. 100–114 (2002)

  32. 32.

    Geyer-Schulz, A., Hahsler, M., Jahn, M.: A customer purchase incidence model applied to recommender services. In: Proceedings of the 3rd International Workshop on Mining Web Log Data Across All Customers Touch Points, pp. 25–47 (2002)

  33. 33.

    Geyer-Schulz, A., Hahsler, M., Jahn, M.: Recommendations for virtual universities from observed user behavior. In: Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., pp. 273–280 (2002)

  34. 34.

    Geyer-Schulz, A., Hahsler, M., Jahn, M., Geyer, A.: Wissenschaftliche Recommendersysteme in Virtuellen Universitäten. In: Proceedings of the Symposiom of Unternehmen Hochschule, pp. 101–114 (2001)

  35. 35.

    Geyer-Schulz, A., Hahsler, M., Neumann, A., Thede, A.: An integration strategy for distributed recommender services in legacy library systems. In: Between Data Science and Applied Data Analysis. Springer, pp. 412–420 (2003)

  36. 36.

    Geyer-Schulz, A., Hahsler, M., Neumann, A., Thede, A.: Behavior-based recommender systems as value-added services for scientific libraries. Statistical Data Mining and Knowledge Discovery, pp. 433–454 (2003)

  37. 37.

    Geyer-Schulz, A., Hahsler, M., Thede, A.: Comparing Simple Association-Rules and Repeat-Buying Based Recommender Systems in a B2B Environment. In: Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., pp. 421–429 (2003)

  38. 38.

    Geyer-Schulz, A., Neumann, A., Thede, A.: An architecture for behavior-based library recommender systems. Inf. Technol. Libr. 22(4), 165–174 (2003)

    MATH  Google Scholar 

  39. 39.

    Geyer-Schulz, A., Neumann, A., Thede, A.: Others also use: a robust recommender system for scientific libraries. In: Proceedings of the 7th European Conference on Digital Libraries, pp. 113–125 (2003)

  40. 40.

    Gillitzer, B.: Der Empfehlungsdienst BibTip - Ein flächendeckendes Angebot im Bibliotheksverbund Bayern. http://www.b-i-t-online.de/heft/2010-01/nachrichtenbeitrag3. pp. 1–4 (2010)

  41. 41.

    Gottwald, S.: Recommender Systeme fuer den Einsatz in Bibliotheken/Survey on recommender systems. Konrad-Zuse-Zentrum für Informationstechnik Berlin, ZIB-Report 11–30 (2011)

  42. 42.

    Geyer-Schulz, A., Hahsler, M., Jahn, M.: Educational and scientific recommender systems: designing the information channels of the virtual university. Int. J. Eng. Educ. 17(2), 153–163 (2001)

    Google Scholar 

  43. 43.

    Giles, C.L., Bollacker, K.D., Lawrence, S.: CiteSeer: an automatic citation indexing system. In: Proceedings of the 3rd ACM conference on Digital libraries, pp. 89–98 (1998)

  44. 44.

    Gipp, B., Beel, J.: Citation proximity analysis (CPA)—a new approach for identifying related work based on co-citation analysis. In: Proceedings of the 12th international conference on Scientometrics and informetrics (ISSI’09), vol. 2, pp. 571–575 (2009)

  45. 45.

    Gipp, B., Beel, J., Hentschel, C.: Scienstein: a research paper recommender system. In: Proceedings of the international conference on Emerging trends in computing (ICETiC’09), pp. 309–315 (2009)

  46. 46.

    Gori, M., Pucci, A.: Research paper recommender systems: a random-walk based approach. In: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web intelligence, pp. 778–781 (2006)

  47. 47.

    Henning, V., Reichelt, J.: Mendeley-a last. fm for research? In: Proceedings of the IEEE 4th international conference on eScience, pp. 327–328 (2008)

  48. 48.

    Hwang, S.-Y., Hsiung, W.-C., Yang, W.-S.: A prototype WWW literature recommendation system for digital libraries. Online Inf. Rev. 27(3), 169–182 (2003)

    Article  Google Scholar 

  49. 49.

    He, J., Nie, J.-Y., Lu, Y., Zhao, W.X.: Position-aligned translation model for citation recommendation. In: Proceedings of the 19th international conference on String processing and information retrieval, pp. 251–263 (2012)

  50. 50.

    He, Q., Kifer, D., Pei, J., Mitra, P., Giles, C.L.: Citation recommendation without author supervision. In: Proceedings of the 4th ACM international conference on Web search and data mining, pp. 755–764 (2011)

  51. 51.

    He, Q., Pei, J., Kifer, D., Mitra, P., Giles, L.: Context-aware citation recommendation. In: Proceedings of the 19th international conference on World wide web, pp. 421–430 (2010)

  52. 52.

    Hess, C.: Trust-Based Recommendations in Multi-Layer Networks. IOS Press, Amsterdam (2008)

    Google Scholar 

  53. 53.

    Hess, C.: Trust-based recommendations for publications: a multi-layer network approach. TCDL Bull. 2(2), 190–201 (2006)

    Google Scholar 

  54. 54.

    Hess, C., Stein, K., Schlieder, C.: Trust-enhanced visibility for personalized document recommendations. In: Proceedings of the 2006 ACM symposium on Applied computing, pp. 1865–1869 (2006)

  55. 55.

    Huang, S., Xue, G.R., Zhang, B.Y., Chen, Z., Yu, Y., Ma, W.Y.: Tssp: a reinforcement algorithm to find related papers. In: Proceedings of the IEEE/WIC/ACM international conference on Web intelligence (WI), pp. 117–123 (2004)

  56. 56.

    Huang, W., Kataria, S., Caragea, C., Mitra, P., Giles, C.L., Rokach, L.: Recommending citations: translating papers into references. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp. 1910–1914 (2012)

  57. 57.

    Huang, Z., Chung, W., Ong, T.H., Chen, H.: A graph-based recommender system for digital library. In: Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries, pp. 65–73 (2002)

  58. 58.

    Jack, K.: Mendeley: recommendation systems for academic literature. Presentation at Technical University of Graz (TUG) (2012)

  59. 59.

    Jack, K.: Mendeley suggest: engineering a personalised article recommender system. Presentation at RecSysChallenge workshop 2012 (2012)

  60. 60.

    Jack, K.: Mahout becomes a researcher: large scale recommendations at Mendeley. Presentation at big data week conferences (2012)

  61. 61.

    Jiang, Y., Jia, A., Feng, Y., Zhao, D.: Recommending academic papers via users’ reading purposes. In: Proceedings of the 6th ACM conference on Recommender systems, pp. 241–244 (2012)

  62. 62.

    Jomsri, P., Sanguansintukul, S., Choochaiwattana, W.: A framework for tag-based research paper recommender system: an IR approach. In: Proceedings of the 24th international conference on Advanced information networking and applications (WAINA), pp. 103–108 (2010)

  63. 63.

    Kapoor, N., Chen, J., Butler, J.T., Fouty, G.C., Stemper, J.A., Riedl, J., Konstan, J.A.: Techlens: a researcher’s desktop. In: Proceedings of the 2007 ACM conference on Recommender systems, pp. 183–184 (2007)

  64. 64.

    Konstan, J.A., Kapoor, N., McNee, S.M., Butler, J.T.: Techlens: exploring the use of recommenders to support users of digital libraries. In: Proceedings of the coalition for networked information fall 2005 task force meeting, pp. 111–112 (2005)

  65. 65.

    Kataria, S., Mitra, P., Bhatia, S.: Utilizing context in generative bayesian models for linked corpus. In: Proceedings of the 24th AAAI conference on Artificial intelligence, pp. 1340–1345 (2010)

  66. 66.

    Kodakateri Pudhiyaveetil, A., Gauch, S., Luong, H., Eno, J.: Conceptual recommender system for CiteSeerX. In: Proceedings of the 3rd ACM conference on Recommender systems, pp. 241–244 (2009)

  67. 67.

    Kuberek, M., Mönnich, M.: Einsatz von Recommendersystemen in Bibliotheken Recommender systems in libraries. Presentation (2012)

  68. 68.

    Küçüktunç, O., Kaya, K., Saule, E., Catalyürek, U.V.: Fast recommendation on bibliographic networks. In: Proceedings of the IEEE/ACM international conference on Advances in social networks analysis and mining (ASONAM), pp. 480–487 (2012)

  69. 69.

    Küçüktunç, O., Kaya, K., Saule, E., Catalyürek, U.V.: Fast recommendation on bibliographic networks with sparse-matrix ordering and partitioning. Soc. Netw. Anal. Min. 3(4), 1097–1111 (2013)

    Article  Google Scholar 

  70. 70.

    Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, Ü.V.: Result Diversification in automatic citation recommendation. In: Proceedings of the iConference workshop on Computational scientometrics: theory and applications, pp. 1–4 (2013)

  71. 71.

    Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, Ü.V.: Diversifying citation recommendations. arXiv preprint. arXiv:1209.5809. pp. 1–19 (2012)

  72. 72.

    Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, Ü.V.: Recommendation on academic networks using direction aware citation analysis. arXiv preprint. arXiv:1205.1143. pp. 1–10 (2012)

  73. 73.

    Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, Ü.V.: Direction awareness in citation recommendation. In: Proceedings of DBRank workshop in conjunction with VLDB’12. pp. 161–166 (2012)

  74. 74.

    Lao, N.: Efficient random walk inference with knowledge bases. PhD Thesis. The Carnegie Mellon University (2012)

  75. 75.

    Lao, N., Cohen, W.W.: Personalized reading recommendations for Saccharomyces genome database. Unpublished Paper. http://www.cs.cmu.edu/nlao/publication/2012/2012.dils.pdf. pp. 1–15 (2012)

  76. 76.

    Lao, N., Cohen, W.W.: Personalized reading recommendations for Saccharomyces genome database. Unpublished Poster. http://www.cs.cmu.edu/nlao/publication/2012/2012.dils.poster.portrat.pdf (2012)

  77. 77.

    Lao, N., Cohen, W. W.: Contextual recommendation with path constrained random walks. Unpublished. http://www.cs.cmu.edu/nlao/doc/2011.cikm.pdf. pp. 1–9 (2011)

  78. 78.

    Lakkaraju, P., Gauch, S., Speretta, M.: Document similarity based on concept tree distance. In: Proceedings of the 19th ACM conference on Hypertext and hypermedia, pp. 127–132 (2008)

  79. 79.

    Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)

    MathSciNet  Article  Google Scholar 

  80. 80.

    Lawrence, K.D.B.S.: A system for automatic personalized tracking of scientific literature on the web. In: Proceedings of the 4th ACM conference on Digital libraries, pp. 105–113 (1999)

  81. 81.

    Lawrence, S.R., Bollacker, K.D., Giles, C.L.: Autonomous citation indexing and literature browsing using citation context. U.S. Patent US 6,738,780 B2Summer-2004

  82. 82.

    Lawrence, S.R., Giles, C. L., Bollacker, K.D.: Autonomous citation indexing and literature browsing using citation context. U.S. Patent US 6,289,342 B1Nov-2001

  83. 83.

    Li, H., Councill, I., Lee, W.-C., Giles, C. L.: CiteSeerx: an architecture and web service design for an academic document search engine. In: Proceedings of the 15th international conference on World wide web, pp. 883–884 (2006)

  84. 84.

    Liang, Y., Li, Q., Qian, T.: Finding relevant papers based on citation relations. In: Proceedings of the 12th international conference on Web-age information management, pp. 403–414 (2011)

  85. 85.

    Lin, J., Wilbur, W.J.: PubMed related articles: a probabilistic topic-based model for content similarity. BMC Bioinform. 8(1), 423–436 (2007)

  86. 86.

    Lu, Y., He, J., Shan, D., Yan, H.: Recommending citations with translation model. In: Proceedings of the 20th ACM international conference on Information and knowledge management, pp. 2017–2020 (2011)

  87. 87.

    McNee, S. M., Kapoor, N., Konstan, J.A.: Don’t look stupid: avoiding pitfalls when recommending research papers. In: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pp. 171–180 (2006)

  88. 88.

    Middleton, S.E., Alani, H., De Roure, D.C.: Exploiting synergy between ontologies and recommender systems. In: Proceedings of the semantic web workshop, pp. 1–10 (2002)

  89. 89.

    Middleton, S.E., De Roure, D., Shadbolt, N.R.: Ontology-based recommender systems. In: Handbook on Ontologies, pp. 779–796, Springer, Berlin (2009)

  90. 90.

    Middleton, S.E., De Roure, D.C., Shadbolt, N.R.: Foxtrot recommender system: user profiling, ontologies and the World Wide Web. In: Proceedings of the WWW conference, pp. 1–3 (2002)

  91. 91.

    Middleton, S.E., De Roure, D.C., Shadbolt, N.R.: Capturing knowledge of user preferences: ontologies in recommender systems. In: Proceedings of the 1st international conference on Knowledge capture, pp. 100–107 (2001)

  92. 92.

    Mönnich, M., Spiering, M.: Adding value to the library catalog by implementing a recommendation system. D-Lib Mag. 14(5), 4–11 (2008)

    Google Scholar 

  93. 93.

    McNee, S.M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S.K., Rashid, A.M., Konstan, J.A., Riedl, J.: On the recommending of citations for research papers. In: Proceedings of the ACM conference on Computer supported cooperative work, pp. 116–125 (2002)

  94. 94.

    Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 54–88 (2004)

    Article  Google Scholar 

  95. 95.

    Monnich, M., Spiering, M.: Einsatz von BibTip als Recommendersystem im Bibliothekskatalog. Bibliotheksdienst 42(1), 54 (2008)

    Article  Google Scholar 

  96. 96.

    Naak, A.: Papyres: un système de gestion et de recommandation d’articles de recherche. Master Thesis. Université de Montréal (2009)

  97. 97.

    Neumann, A.W.: Recommender Systems for Information Providers. Springer, Berlin (2009)

    Google Scholar 

  98. 98.

    Naak, A., Hage, H., Aimeur, E.: A multi-criteria collaborative filtering approach for research paper recommendation in papyres. In: Proceedings of the 4th international conference MCETECH, pp. 25–39 (2009)

  99. 99.

    Naak, A., Hage, H., Aimeur, E.: Papyres: a research paper management system. In: Proceedings of the 10th E-Commerce Technology Conference on Enterprise Computing, E-Commerce and E-Services, pp. 201–208 (2008)

  100. 100.

    Nallapati, R.M., Ahmed, A., Xing, E.P., Cohen, W.W.: Joint latent topic models for text and citations. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 542–550 (2008)

  101. 101.

    Nascimento, C., Laender, A.H., da Silva, A.S., Gonçalves, M.A.: A source independent framework for research paper recommendation. In: Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries, pp. 297–306 (2011)

  102. 102.

    Ozono, T., Goto, S., Fujimaki, N., Shintani, T.: P2p based knowledge source discovery on research support system papits. In: Proceedings of the 1st international joint conference on Autonomous agents and multiagent systems: part 1, pp. 49–50 (2002)

  103. 103.

    Ozono, T., Shintani, T.: P2P based information retrieval on research support system Papits. In: Proceedngs of the IASTED international conference on Artificial and computational intelligence, pp. 136–141 (2002)

  104. 104.

    Ozono, T., Shintani, T.: Paper classification for recommendation on research support system papits. IJCSNS Int. J. Comput. Sci. Netw. Secur. 6, 17–23 (2006)

    Google Scholar 

  105. 105.

    Ozono, T., Shintani, T., Ito, T., Hasegawa, T.: A feature selection for text categorization on research support system Papits. In: Proceedings of the 8th Pacific Rim international conference on Artificial intelligence, pp. 524–533 (2004)

  106. 106.

    Pennock, D.M., Horvitz, E., Lawrence, S., Giles, C.L.: Collaborative filtering by personality diagnosis: a hybrid memory-and model-based approach. In: Proceedings of the 16th conference on Uncertainty in artificial intelligence, pp. 473–480 (2000)

  107. 107.

    Petinot, Y., Giles, C.L., Bhatnagar, V., Teregowda, P.B., Han, H.: Enabling interoperability for autonomous digital libraries: an API to citeseer services. In: Digital Libraries, 2004. Proceedings of the 2004 joint ACM/IEEE conference on, pp. 372–373 (2004)

  108. 108.

    Petinot, Y., Giles, C.L., Bhatnagar, V., Teregowda, P.B., Han, H., Councill, I.: A service-oriented architecture for digital libraries. In: Proceedings of the 2nd international conference on Service oriented computing, pp. 263–268 (2004)

  109. 109.

    Pohl, S.: Using access data for paper recommendations on ArXiv. org. Master Thesis. Technical University of Darmstadt (2007)

  110. 110.

    Pohl, S., Radlinski, F., Joachims, T.: Recommending related papers based on digital library access records. In: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, pp. 417–418 (2007)

  111. 111.

    Researchgate, T.: Researchgate recommender. http://www.researchgate.net/directory/publications/ (2011)

  112. 112.

    Rokach, L., Mitra, P., Kataria, S., Huang, W., Giles, L.: A supervised learning method for context-aware citation recommendation in a large corpus. In: Proceedings of the large-scale and distributed systems for information retrieval workshop (LSDS-IR), pp. 17–22 (2013)

  113. 113.

    Sarkanto: About the Sarkanto Recommender Demo. http://lab.cisti-icist.nrc-cnrc.gc.ca/Sarkanto/about.jsp (2013)

  114. 114.

    Strohman, T., Croft, W.B., Jensen, D.: Recommending citations for academic papers. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 705–706 (2007)

  115. 115.

    Sugiyama, K., Kan, M.-Y.: Scholarly paper recommendation via user’s recent research interests. In: Proceedings of the 10th ACM/IEEE annual joint conference on Digital libraries (JCDL), pp. 29–38 (2010)

  116. 116.

    Thomas, D., Greenberg, A., Calarco, P.: Scholarly usage based recommendations: evaluating bX for a Consortium, Presentation. http://igelu.org/wp-content/uploads/2011/09/bx_igelu_presentation_updated_september-13.pdf (2011)

  117. 117.

    Torres, R., McNee, S.M., Abel, M., Konstan, J.A., Riedl, J.: Enhancing digital libraries with TechLens+. In: Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries, 2004, pp. 228–236

  118. 118.

    Uchiyama, K., Nanba, H., Aizawa, A., Sagara, T.: OSUSUME: cross-lingual recommender system for research papers. In: Proceedings of the 2011 workshop on context-awareness in retrieval and recommendation, pp. 39–42 (2011)

  119. 119.

    Vellino, A.: A comparison between usage-based and citation-based methods for recommending scholarly research articles. Proc. Am. Soc. Inf. Sci. Technol. 47(1), 1–2 (2010)

  120. 120.

    Vellino, A., Zeber, D.: A hybrid, multi-dimensional recommender for journal articles in a scientific digital library. In: Proceedings of the 2007 IEEE/WIC/ACM international conference on Web intelligence, pp. 111–114 (2007)

  121. 121.

    Wang, Y., Zhai, E., Hu, J., Chen, Z.: Claper: recommend classical papers to beginners. Seventh international conference on Fuzzy systems and knowledge discovery 6, 2777–2781 (2010)

  122. 122.

    Watanabe, S., Ito, T., Ozono, T., Shintani, T.: A paper recommendation mechanism for the research support system papits. In: Proceedings of the international workshop on Data engineering issues in E-Commerce, pp. 71–80

  123. 123.

    Woodruff, A., Gossweiler, R., Pitkow, J., Chi, E.H., Card, S.K.: Enhancing a digital book with a reading recommender. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 153–160 (2000)

  124. 124.

    Yang, C., Wei, B., Wu, J., Zhang, Y., Zhang, L.: CARES: a ranking-oriented CADAL recommender system. In: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries, pp. 203–212 (2009)

  125. 125.

    Zarrinkalam, F., Kahani, M.: SemCiR—a citation recommendation system based on a novel semantic distance measure. Program: Electron. Libr. Inf. Syst. 47(1), 92–112 (2013)

    Google Scholar 

  126. 126.

    Zarrinkalam, F., Kahani, M.: A new metric for measuring relatedness of scientific papers based on non-textual features. Intell. Inf. Manag. 4(4), 99–107 (2012)

    Google Scholar 

  127. 127.

    Zhou, D., Zhu, S., Yu, K., Song, X., Tseng, B.L., Zha, H., Giles, C.L.: Learning multiple graphs for document recommendations. In: Proceedings of the 17th international conference on World Wide Web, pp. 141–150 (2008)

  128. 128.

    Avancini, H., Candela, L., Straccia, U.: Recommenders in a personalized, collaborative digital library environment. J. Intell. Inf. Syst. 28(3), 253–283 (2007)

    Article  Google Scholar 

  129. 129.

    Agarwal, N., Haque, E., Liu, H., Parsons, L.: A subspace clustering framework for research group collaboration. Int. J. Inf. Technol. Web Eng. 1(1), 35–58 (2006)

    Article  Google Scholar 

  130. 130.

    Agarwal, N., Haque, E., Liu, H., Parsons, L.: Research paper recommender systems: a subspace clustering approach. In: Proceedings of the 6th international conference on Advances in Web-Age Information Management (WAIM’05), pp. 475–491 (2005)

  131. 131.

    Bollen, J., Rocha, L.M.: An adaptive systems approach to the implementation and evaluation of digital library recommendation systems. In: Proceedings of the 4th European conference on Digital libraries, Springer, pp. 356–359 (2000)

  132. 132.

    Bancu, C., Dagadita, M., Dascalu, M., Dobre, C., Trausan-Matu, S., Florea, A.M.: ARSYS-article recommender system. In: Proceedings of the 14th international symposium on Symbolic and numeric algorithms for scientific computing, pp. 349–355 (2012)

  133. 133.

    Cazella, S.C., Alvares, L.O.C.: Combining data mining technique and users’ relevance opinion to build an efficient recommender system. Revista Tecnologia da Informação, UCB, 4(2) (2005)

  134. 134.

    Cazella, S., Alvares, L.: Modeling user’s opinion relevance to recommending research papers. In: Proceedings of the UMAP Conference, pp. 150–150 (2005)

  135. 135.

    Chirawatkul, P.: Structured Peer-to-Peer Search to Build a Bibliographic Paper Recommendation System. Saarland University, Saarland (2006)

    Google Scholar 

  136. 136.

    Dattolo, A., Ferrara, F., Tasso, C.: Supporting personalized user concept spaces and recommendations for a publication sharing system. In: Proceedings of the 17th international conference on User modeling, adaptation, and personalization, pp. 325–330 (2009)

  137. 137.

    Daud, A.: Muhammad Akramand Rajpar Shaikh, A.H.: Scientific reference mining using semantic information through topic modeling. Res. J. Eng. Technol. 28(2), 253–262 (2009)

    Google Scholar 

  138. 138.

    Farooq, U., Ganoe, C.H., Carroll, J.M., Councill, I.G.: Lee Giles, C.: Design and evaluation of awareness mechanisms in CiteSeer. Inf. Process. Manag. 44(2), 596–612 (2008)

    Article  Google Scholar 

  139. 139.

    Fernández, L., Sánchez, J.A., García, A.: Mibiblio: personal spaces in a digital library universe. In: Proceedings of the 5th ACM conference on Digital libraries, pp. 232–233 (2000)

  140. 140.

    Gross, T.: CYCLADES: a distributed system for virtual community support based on open archives. In: Proceedings of the 11th Euromicro Conference on Parallel, distributed and network-based orocessing, pp. 484–491 (2003)

  141. 141.

    Geisler, G., McArthur, D., Giersch, S.: Developing recommendation services for a digital library with uncertain and changing data. In: Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries, pp. 199–200 (2001)

  142. 142.

    Hong, K., Jeon, H., Jeon, C.: UserProfile-based personalized research paper recommendation system. In: Proceedings of the 8th international conference on Computing and networking technology, pp. 134–138 (2012)

  143. 143.

    Huang, Y.: Combining Social Networks and Content for Recommendation in a Literature Digital Library. National Sun Yat-Sen University, Taiwan (2007)

    Google Scholar 

  144. 144.

    Kang, S., Cho, Y.: A novel personalized paper search system. In: Proceedings of the international conference on Intelligent computing, pp. 1257–1262 (2006)

  145. 145.

    Martin, G.H., Schockaert, S., Cornelis, C., Naessens, H.: Metadata impact on research paper similarity. In: 14th European Conference on Digital libraries, pp. 457–460 (2010)

  146. 146.

    Morales-del-Castillo, J.M., Peis, E., Herrera-Viedma, E.: A filtering and recommender system prototype for scholarly users of digital libraries. In: Proceedings of the Second World Summit on the Knowledge Society, Springer, pp. 108–117 (2009)

  147. 147.

    Mao, Y., Vassileva, J., Grassmann, W.: A system dynamics approach to study virtual communities. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences, pp. 178–197 (2007)

  148. 148.

    Matsatsinis, N.F., Lakiotaki, K., Delia, P.: A system based on multiple criteria analysis for scientific paper recommendation. In: Proceedings of the 11th Panhellenic Conference on Informatics, pp. 135–149 (2007)

  149. 149.

    Mishra, G.: Optimised research paper recommender system using social tagging. Int. J. Eng. Res. Appl. 2(2), 1503–1507 (2012)

    Google Scholar 

  150. 150.

    Nakagawa, A., Ito, T.: An implementation of a knowledge recommendation system based on similarity among users’ profiles. In: Proceedings of the 41st SICE annual conference, vol. 1, pp. 326–327 (2002)

  151. 151.

    Pan, C., Li, W.: Research paper recommendation with topic analysis. In: Proceedings of the international conference on Computer design and applications (ICCDA), pp. 264–268 (2010)

  152. 152.

    Popa, H.-E., Negru, V., Pop, D., Muscalagiu, I.: DL-AgentRecom-A multi-agent based recommendation system for scientific documents. In: Proceedings of the 10th international symposium on Symbolic and numeric algorithms for scientific computing, pp. 320–324 (2008)

  153. 153.

    Ratprasartporn, N., Ozsoyoglu, G.: Finding related papers in literature digital libraries. In: Proceedings of the 11th European Conference on Digital libraries, pp. 271–284 (2007)

  154. 154.

    Rocha, L.M.: TalkMine: a soft computing approach to adaptive knowledge recommendation. Stud. Fuzziness Soft Comput. 75, 89–116 (2001)

    Article  Google Scholar 

  155. 155.

    Rocha, L.M.: Talkmine and the adaptive recommendation project. In: Proceedings of the fourth ACM conference on Digital libraries, pp. 242–243 (1999)

  156. 156.

    Stock, K., Robertson, A., Reitsma, F., Stojanovic, T., Bishr, M., Medyckyj-Scott, D., Ortmann, J.: eScience for Sea Science: a semantic scientific knowledge infrastructure for marine scientists. In: Proceedings of the 5th IEEE international conference on e-Science, pp. 110–117 (2009)

  157. 157.

    Straccia, U.: Cyclades: an open collaborative virtual archive environment. Poster (http://www.ercim.eu/cyclades/cyclades-fs.pdf) (2003)

  158. 158.

    Shaoping, Z.: ActiveCite: an interactive system for automatic citation suggestion. Master Thesis. National University of Singapore (2010)

  159. 159.

    Stock, K., Karasova, V., Robertson, A., Roger, G., Small, M., Bishr, M., Ortmann, J., Stojanovic, T., Reitsma, F., Korczynski, L., Brodaric, B., Gardner, Z.: Finding science with science: evaluating a domain and scientific ontology user interface for the discovery of scientific resources. Trans. GIS 1, 1–28 (2013)

    Google Scholar 

  160. 160.

    Tang, T.Y., McCalla, G.: Towards pedagogy-oriented paper recommendations and adaptive annotations for a web-based learning system. In: Knowledge representation and automated reasoning for E-Learning systems, pp. 72–80 (2003)

  161. 161.

    Tang, J., Zhang, J.: A discriminative approach to topic-based citation recommendation. Advances in Knowledge Discovery and Data Mining, pp. 572–579 (2009)

  162. 162.

    Tang, T., McCalla, G.: Utilizing artificial learners to help overcome the cold-start problem in a pedagogically-oriented paper recommendation system. In: Adaptive hypermedia and adaptive web-based systems, pp. 245–254 (2004)

  163. 163.

    Tang, T., McCalla, G.: Beyond learners’ interest: personalized paper recommendation based on their pedagogical features for an e-learning system. In: Proceedings of the 8th Pacific Rim international conference on Artificial intelligence, Springer, pp. 301–310 (2004)

  164. 164.

    Tang, T.Y., McCalla, G.: Mining implicit ratings for focused collaborative filtering for paper recommendations. In: Proceedings of the workshop on User and group models for web-based adaptive collaborative environments (2003)

  165. 165.

    Tang, T.Y., McCalla, G.: Smart recommendation for an evolving e-learning system. In: Proceedings of the workshop on Technologies for electronic documents for supporting learning, at the international conference on Artificial intelligence in education, pp. 699–710 (2003)

  166. 166.

    Tang, T.Y.: The design and study of pedagogical paper recommendation. PhD Thesis. University of Saskatchewan (2008)

  167. 167.

    Tang, T.Y., McCalla, G.: A multidimensional paper recommender: experiments and evaluations. Internet Comput. IEEE 13(4), 34–41 (2009)

  168. 168.

    Tang, T.Y., McCalla, G.: The pedagogical value of papers: a collaborative-filtering based paper recommender. J. Digit. Inf. 10(2), 1–12 (2009)

    Google Scholar 

  169. 169.

    Tang, T.Y., McCalla, G.: On the pedagogically guided paper recommendation for an evolving web-based learning system. In: Proceedings of the FLAIRS Conference, pp. 86–91 (2004)

  170. 170.

    Tang, T.Y., McCalla, G.: The social affordance of a paper. In: Proceedings of the workshop of assessment of group and individual learning through intelligent visualization on the 13th international conference on Artificial intelligence in education, pp. 34–42 (2007)

  171. 171.

    Tang, X., Zeng, Q.: Keyword clustering for user interest profiling refinement within paper recommender systems. J. Syst. Softw. 85(1), 87–101 (2012)

    Article  Google Scholar 

  172. 172.

    Vassileva, J.: Harnessing p2p power in the classroom. In: Proceedings of the conference on Intelligent tutoring systems, pp. 305–314 (2004)

  173. 173.

    Vassileva, J.: Supporting peer-to-peer user communities. In: Proceedings of the conference on the move to meaningful internet systems, pp. 230–247 (2002)

  174. 174.

    Vassileva, J., Detters, R., Geer, J., Maccalla, G., Bull, S., Kettel, L.: Lessons from deploying I-Help. In: Workshop on Multi-agent architectures for distributed learning environments. In: Proceedings of international conference on AI and Education, San Antonio, TX, pp. 3–11 (2001)

  175. 175.

    Vivacqua, A.S., Oliveira, J., de Souza, J.M.: i-ProSE: inferring user profiles in a scientific context. Comput. J. 52(7), 789–798 (2009)

    Article  Google Scholar 

  176. 176.

    Weng, S.-S., Chang, H.-L.: Using ontology network analysis for research document recommendation. Expert Syst. Appl. 34(3), 1857–1869 (2008)

    Article  Google Scholar 

  177. 177.

    Winoto, P., Tang, T.Y., McCalla, G.I.: Contexts in a paper recommendation system with collaborative filtering. Int. Rev. Res. Open Distance Learn. 13(5), 56–75 (2012)

    Google Scholar 

  178. 178.

    Wu, H., Hua, Y., Li, B., Pei, Y.: Enhancing citation recommendation with various evidences. In: Proceedings of the 9th international conference on Fuzzy systems and knowledge discovery (FSKD), pp. 1160–1165 (2012)

  179. 179.

    Xia, H., Li, J., Tang, J., Moens, M.-F.: Plink-LDA: using link as prior information in topic modeling. In: Proceedings of the conference on Database systems for advanced applications (DASFAA), pp. 213–227 (2012)

  180. 180.

    Yang, Q., Zhang, S., Feng, B.: Research on personalized recommendation system of scientific and technological periodical based on automatic summarization. In: Proceedings of the 1st international symposium on Information technologies and applications in education, pp. 34–39 (2007)

  181. 181.

    Yang, S.-Y., Hsu, C.-L.: A new ontology-supported and hybrid recommending information system for scholars. In: Proceedings of the 13th international conference on Network-based information systems (NBiS), pp. 379–384 (2010)

  182. 182.

    Yin, P., Zhang, M., Li, X.: Recommending scientific literatures in a collaborative tagging environment. In: Proceedings of the 10th international conference on Asian digital libraries, Springer, pp. 478–481 (2007)

  183. 183.

    Zarrinkalam, F., Kahani, M.: A multi-criteria hybrid citation recommendation system based on linked data. In: Proceedings of the 2nd international eConference on Computer and knowledge engineering, pp. 283–288 (2012)

  184. 184.

    Zhang, M., Wang, W., Li, X.: A paper recommender for scientific literatures based on semantic concept similarity. In: Proceedings of the international conference on Asian Digital Libraries, pp. 359–362 (2008)

  185. 185.

    Zhang, Z., Li, L.: A research paper recommender system based on spreading activation model. In: Proceedings of the 2nd international conference on Information Science and Engineering (ICISE), pp. 928–931 (2010)

  186. 186.

    Gottwald, S., Koch, T.: Recommender systems for libraries. In: Proceedings of the ACM international conference on Recommender systems, pp. 1–5 (2011)

  187. 187.

    Leong, S.: A survey of recommender systems for scientific papers. Presentation. http://www.liquidpub.org/mediawiki/upload/f/ff/RecommenderSystems.pdf (2012)

  188. 188.

    Smeaton, A.F., Callan, J.: Personalisation and recommender systems in digital libraries. Int. J. Digit. Libr. 5(4), 299–308 (2005)

    Article  Google Scholar 

  189. 189.

    Alotaibi, S., Vassileva, J.: Trust-based recommendations for scientific papers based on the researcher’s current interest. In: Artificial Intelligence in Education, pp. 717–720 (2013)

  190. 190.

    Beel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., Nürnberger, A.: Research paper recommender system evaluation: a quantitative literature survey. In: Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys), pp. 15–22 (2013)

  191. 191.

    Beel, J., Langer, S., Genzmehr, M., Gipp, B., Nürnberger, A.: A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. In: Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys), pp. 7–14 (2013)

  192. 192.

    Chen, C., Mao, C., Tang, Y., Chen, G., Zheng, J.: Personalized recommendation based on implicit social network of researchers. In: Joint international conference, ICPCA/SWS, pp. 97–107 (2013)

  193. 193.

    De Nart, D., Ferrara, F., Tasso, C.: Personalized access to scientific publications: from recommendation to explanation. In: Proceedings of the international conference on User modeling, adaptation, and personalization, pp. 296–301 (2013)

  194. 194.

    De Nart, D., Ferrara, F., Tasso, C.: RES: a personalized filtering tool for CiteSeerX queries based on keyphrase extraction. In: Proceedings of the international conference on User modeling, adaptation, and personalization (UMAP), pp. 341–343 (2013)

  195. 195.

    Franke, M., Geyer-Schulz, A., Neumann, A.: Building recommendations from random walks on library opac usage data. In: Data Analysis, Classification and the Forward Search, Springer, pp. 235–246 (2006)

  196. 196.

    Kim, S.: iScholar: a mobile research support system. PhD Thesis. University of Regina (2013)

  197. 197.

    Küçüktunç, O.: Result Diversication on Spatial, Multidimensional, Opinion, and Bibliographic Data. Ohio State University, Columbus (2013)

    Google Scholar 

  198. 198.

    Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, Ü. V.: TheAdvisor: a webservice for academic recommendation. In: Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, pp. 433–434 (2013)

  199. 199.

    Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, Ü. V.: Towards a personalized, scalable, and exploratory academic recommendation service. In: Proceedings of the 2013 IEEE/ACM international conference on Advances in social networks analysis and mining, pp. 636–641 (2013)

  200. 200.

    Lai, Y., Zeng, J.: A cross-language personalized recommendation model in digital libraries. Electron. Libr. 31(3), 164–277 (2013)

    MathSciNet  Article  Google Scholar 

  201. 201.

    Li, Y., Yang, M., Zhang, Z.M.: Scientific articles recommendation. In: Proceedings of the 22nd ACM International conference on information and knowledge management, pp. 1147–1156 (2013)

  202. 202.

    Lee, J., Lee, K., Kim, J.G.: Personalized academic research paper recommendation system. ArXiv Preprint, vol. arXiv:1304.5457. pp. 1–8 (2013)

  203. 203.

    Manouselis, N., Verbert, K.: Layered evaluation of multi-criteria collaborative filtering for scientific paper recommendation. Procedia Comput. Sci. 18, 1189–1197 (2013)

    Article  Google Scholar 

  204. 204.

    Meng, F., Gao, D., Li, W., Sun, X., Hou, Y.: A unified graph model for personalized query-oriented reference paper recommendation. In: Proceedings of the 22nd ACM international conference on Conference on information and knowledge management, pp. 1509–1512 (2013)

  205. 205.

    Pera, M.S., Ng, Y.-K.: Exploiting the wisdom of social connections to make personalized recommendations on scholarly articles. J. Intell. Inf. Syst. 42(3), 371–391 (2014)

    Article  Google Scholar 

  206. 206.

    Pera, M.S., Ng, Y.-K.: Exploiting the wisdom of social connections to make personalized recommendations on scholarly articles. J. Intell. Inf. Syst. 42(3), 371–391 (2014)

    Article  Google Scholar 

  207. 207.

    Sugiyama, K., Kan, M.-Y.: Exploiting potential citation papers in scholarly paper recommendation. In: Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, pp. 153–162 (2013)

  208. 208.

    Sun, J., Ma, J., Liu, X., Liu, Z., Wang, G., Jiang, H., Silva, T.: A novel approach for personalized article recommendation in online scientific communities. In: Proceedings of the 46th Hawaii international conference on System sciences (HICSS) (2013)

  209. 209.

    Sun, J., Ma, J., Liu, Z., Miao, Y.: Leveraging content and connections for scientific article recommendation. Comput. J. 60–71 (2013)

  210. 210.

    Tian, G., Jing, L.: Recommending scientific articles using bi-relational graph-based iterative RWR. In: Proceedings of the 7th ACM conference on Recommender systems, pp. 399–402 (2013)

  211. 211.

    Vellino, A.: Usage-based vs. citation-based methods for recommending scholarly research articles. Arxiv, vol. arXiv:1303.7149 (2013)

  212. 212.

    Yan, R., Yan, H. et al.: Guess what you will cite: personalized citation recommendation based on users’s preference. In: Proceedings of the annual I&R training and education conference, pp. 428–439 (2013)

  213. 213.

    Yang, W.-S., Lin, Y.-R.: A task-focused literature recommender system for digital libraries. Online Inf. Rev. 37(4), 581–601 (2013)

  214. 214.

    Yao, W., He, J., Huang, G., Cao, J., Zhang, Y.: Personalized recommendation on multi-layer context graph. In: Web Information Systems Engineering (WISE 2013), pp. 135–148 (2013)

  215. 215.

    Yu, L., Yang, J., Yang, D., Yang, X.: A decision support system for finding research topic based on paper recommendation. In: Proceedings of the Pacific Asia conference on Information systems (2013)

  216. 216.

    Zarrinkalam, F., Kahani, M.: Using semantic relations to improve quality of a citation recommendation system. Soft Comput. J. 1(2), 36–45 (2013)

    Google Scholar 

  217. 217.

    Zhang, Z.P., Li, L.N., Yu, H.Y.: A hybrid document recommender algorithm based on random walk. Appl. Mech. Mater. 2270, 336–338 (2013)

    Article  Google Scholar 

  218. 218.

    Beel, J., Gipp, B.: Academic search engine spam and Google Scholar’s resilience against it. J. Electron. Publ. 13(3) (2010)

  219. 219.

    Bar-Ilan, J.: Which h-index?—A comparison of WoS. Scopus Google Scholar Scientometr. 74(2), 257–271 (2007)

    MathSciNet  Google Scholar 

  220. 220.

    Noruzi, A.: Google Scholar: the new generation of citation indexes. Libri 55(4), 170–180 (2005)

    Article  Google Scholar 

  221. 221.

    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186 (1994)

  222. 222.

    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the National Conference on Artificial Intelligence, pp. 187–192 (2002)

  223. 223.

    Shi, Y., Larson, M., Hanjalic, A.: Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput. Surv. 47(1), 3:1–3:45 (2014)

  224. 224.

    Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009)

    MathSciNet  MATH  Google Scholar 

  225. 225.

    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)

    Article  Google Scholar 

  226. 226.

    Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the 4th ACM conference on Recommender systems, pp. 257–260 (2010)

  227. 227.

    Ritchie, A., Teufel, S., Robertson, S.: Using terms from citations for IR: some first results. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) Advances in Information Retrieval, pp. 211–221. Springer (2008)

  228. 228.

    Ritchie, A., Teufel, S., Robertson, S.: Using terms from citations for IR: some first results. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) Advances in Information Retrieval, pp. 211–221. Springer (2008)

  229. 229.

    Ritchie, A.: Citation context analysis for information retrieval. PhD Thesis. University of Cambridge (2008)

  230. 230.

    Dumais, S.T., Nielsen, J.: Automating the assignment of submitted manuscripts to reviewers. In: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 233–244 (1992)

  231. 231.

    Wang, F., Shi, N., Chen, B.: A comprehensive survey of the reviewer assignment problem. Int. J. Inf. Technol. Decis. Mak. 9(04), 645–668 (2010)

    Article  MATH  Google Scholar 

  232. 232.

    Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. U. S. Am. 102(46), 16569 (2005)

    Article  Google Scholar 

  233. 233.

    Small, H.: Co-citation in the scientific literature: a new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 24, 265–269 (1973)

    Article  Google Scholar 

  234. 234.

    Kessler, M.M.: Bibliographic coupling between scientific papers. Am. Documentation 14, 10–25 (1963)

    Article  Google Scholar 

  235. 235.

    Zyczkowski, K.: Citation graph, weighted impact factors and performance indices. Scientometrics 85(1), 301–315 (2010)

    Article  Google Scholar 

  236. 236.

    Fischer, G.: User modeling in human–computer interaction. User Model. User-Adapt. Interact. 11(1), 65–86 (2001)

    Article  MATH  Google Scholar 

  237. 237.

    Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Trans. Internet Technol. (TOIT) 3(1), 1–27 (2003)

    Article  Google Scholar 

  238. 238.

    Pierrakos, D., Paliouras, G., Papatheodorou, C., Spyropoulos, C.D.: Web usage mining as a tool for personalization: a survey. User Model. User-Adapt. Interact. 13(4), 311–372 (2003)

    Article  Google Scholar 

  239. 239.

    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM conference on Digital libraries, pp. 195–204 (2000)

  240. 240.

    Brusilovsky, P., Farzan, R., Ahn, J.: Comprehensive personalized information access in an educational digital library. In: Digital Libraries, 2005. JCDL’05. In: Proceedings of the 5th ACM/IEEE-CS joint conference on, pp. 9–18 (2005)

  241. 241.

    Faensen, D., Faultstich, L., Schweppe, H., Hinze, A., Steidinger, A.: Hermes: a notification service for digital libraries. In: Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries, pp. 373–380 (2001)

  242. 242.

    Das, S., Mitra, P., Giles, C.L.: Similar researcher search’. In: Academic Environments. In: Proceedings of the JCDL’12, pp. 167–170 (2012)

  243. 243.

    Abu-Jbara, A., Radev, D.: Coherent citation-based summarization of scientific papers. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 500–509 (2011)

  244. 244.

    Mohammad, S., Dorr, B., Egan, M., Hassan, A., Muthukrishan, P., Qazvinian, V., Radev, D., Zajic, D.: Using citations to generate surveys of scientific paradigms. In: Proceedings of human language technologies: the 2009 annual conference of the North American Chapter of the Association for Computational Linguistics, 2009, pp. 584–592

  245. 245.

    Teufel, S., Moens, M.: Summarizing scientific articles: experiments with relevance and rhetorical status. Comput. Linguist. 28(4), 409–445 (2002)

    Article  Google Scholar 

  246. 246.

    Collins, L.M., Mane, K.K., Martinez, M.L., Hussell, J.A., Luce, R.E.: ScienceSifter: facilitating activity awareness in collaborative research groups through focused information feeds. In: 1st international conference on e-Science and grid computing, pp. 40–47 (2005)

  247. 247.

    Klamma, R., Cuong, P.M., Cao, Y.: You never walk alone: recommending academic events based on social network analysis. In: Zhou, J. (ed.) Complex Sciences, pp. 657–670. Springer (2009)

  248. 248.

    Klamma, R., Cuong, P.M., Cao, Y.: You never walk alone: recommending academic events based on social network analysis. In: Zhou, J. (ed.) Complex Sciences, pp. 657–670. Springer (2009)

  249. 249.

    Yang, Z., Davison, B. D.: Venue recommendation: submitting your paper with style. In: Machine learning and applications (ICMLA), 2012 11th international conference on, vol. 1, pp. 681–686 (2012)

  250. 250.

    Oh, S., Lei, Z., Lee, W.-C., Mitra, P., Yen, J.: CV-PCR: a context-guided value-driven framework for patent citation recommendation. In: Proceedings of the 22nd ACM international conference on Conference on information and knowledge management, pp. 2291–2296 (2013)

  251. 251.

    Singhal, A., Kasturi, R., Sivakumar, V., Srivastava, J.: Leveraging web intelligence for finding interesting research datasets. In: Web intelligence (WI) and intelligent agent technologies (IAT), 2013 IEEE/WIC/ACM international joint conferences on, vol. 1, pp. 321–328 (2013)

  252. 252.

    Gipp, B., Beel, J.: Citation based plagiarism detection–a new approach to identify plagiarized work language independently. In: Proceedings of the 21st ACM conference on Hypertext and hypermedia, pp. 273–274 (2010)

  253. 253.

    Zhan, S., Byung-Ryul, A., Ki-Yol, E., Min-Koo, K., Jin-Pyung, K., Moon-Kyun, K. (2008) Plagiarism detection using the Levenshtein distance and Smith-Waterman algorithm. In: Proceedings of the 3rd international conference on Innovative computing information and control, pp. 569–569

  254. 254.

    Zini, M., Fabbri, M., Moneglia, M., Panunzi, A.: Plagiarism detection through multilevel text comparison. In: Proceedings of the 2nd conference on Automated production of cross media content for multi-channel distribution, pp. 181–185 (2006)

  255. 255.

    Ley, M., Reuther, P.: Maintaining an online bibliographical database: the problem of data quality, EGC’2006, Actes des sixièmes journées Extraction et Gestion des Connaissances, pp. 17–20 (2006)

  256. 256.

    Beel, J., Langer, S., Genzmehr, M., Müller, C.: Docears PDF inspector: title extraction from PDF files. In: Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries (JCDL’13), pp. 443–444 (2013)

  257. 257.

    Beel, J., Gipp, B., Shaker, A., Friedrich, N.: SciPlore Xtract: extracting titles from scientific PDF documents by analyzing style information (Font Size). In: Research and Advanced Technology for Digital Libraries. Proceedings of the 14th European conference on Digital libraries (ECDL’10), vol. 6273, pp. 413–416 (2010)

  258. 258.

    Han, H., Giles, C.L., Manavoglu, E., Zha, H., Zhang, Z., Fox, E.A.: Automatic document metadata extraction using support vector machines. In: Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, pp. 37–48 (2003)

  259. 259.

    Hu, Y., Li, H., Cao, Y., Teng, L., Meyerzon, D., Zheng, Q.: Automatic extraction of titles from general documents using machine learning. Inf. Process. Manag. 42(5), 1276–1293 (2006)

    Article  Google Scholar 

  260. 260.

    Peng, F., McCallum, A.: Information extraction from research papers using conditional random fields. Inf. Process. Manag. 42(4), 963–979 (2006)

    Article  Google Scholar 

  261. 261.

    Lawrence, S., Giles, C.L., Bollacker, K.D.: Autonomous citation matching. In: Proceedings of the 3rd annual conference on Autonomous agents, pp. 392–393 (1999)

  262. 262.

    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds): Recommender Systems Handbook, pp. 1–35. Springer, Berlin (2011)

  263. 263.

    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds): Recommender Systems Handbook, pp. 1–35. Springer, Berlin (2011)

  264. 264.

    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. Lect. Notes Comput. Sci. 4321, 291 (2007)

    Article  Google Scholar 

  265. 265.

    Rossi, P.H., Lipsey, M.W., Freeman, H.E.: Evaluation: A Aystematic Approach, 7th edn. Sage publications, Thousand Oaks (2004)

    Google Scholar 

  266. 266.

    Gorrell, G., Ford, N., Madden, A., Holdridge, P., Eaglestone, B.: Countering method bias in questionnaire-based user studies. J. Documentation 67(3), 507–524 (2011)

    Article  Google Scholar 

  267. 267.

    Leroy, G.: Designing User Studies in Informatics. Springer, Berlin (2011)

    Google Scholar 

  268. 268.

    Said, A., Tikk, D., Shi, Y., Larson, M., Stumpf, K., Cremonesi, P.: Recommender systems evaluation: a 3d benchmark. In: ACM RecSys 2012 workshop on Recommendation utility evaluation: beyond RMSE, Dublin, Ireland, pp. 21–23 (2012)

  269. 269.

    Cremonesi, P., Garzotto, F., Turrin, R.: Investigating the persuasion potential of recommender systems from a quality perspective: an empirical study. ACM Trans. Interact. Intell. Syst. (TiiS) 2(2), 11 (2012)

    Google Scholar 

  270. 270.

    Cremonesi, P., Garzotto, F., Negro, S., Papadopoulos, A.V., Turrin, R.: Looking for ‘good’ recommendations: a comparative evaluation of recommender systems. In: Human–computer interaction-INTERACT 2011, Springer, pp. 152–168 (2011)

  271. 271.

    Burns, C.A., Bush, F.R.: Marketing Research, 7th edn. Prentice Hall, Upper Saddle River (2013)

    Google Scholar 

  272. 272.

    Loeppky, J.L., Sacks, J., Welch, W.J.: Choosing the sample size of a computer experiment: a practical guide. Technometrics 51(4), 366–376 (2009)

    MathSciNet  Article  Google Scholar 

  273. 273.

    Zheng, H., Wang, D., Zhang, Q., Li, H., Yang, T.: Do clicks measure recommendation relevancy?: an empirical user study. In: Proceedings of the 4th ACM conference on Recommender systems, pp. 249–252 (2010)

  274. 274.

    Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adapt. Interact. 22(1–2), 101–123 (2012)

    Article  Google Scholar 

  275. 275.

    Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adapt. Interact. 22(1–2), 101–123 (2012)

  276. 276.

    Matejka, J., Li, W., Grossman, T., Fitzmaurice, G.: CommunityCommands: command recommendations for software applications. In: Proceedings of the 22nd annual ACM symposium on User interface software and technology, pp. 193–202 (2009)

  277. 277.

    Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th international conference on Intelligent user interfaces, pp. 127–134 (2002)

  278. 278.

    Hersh, W., Turpin, A., Price, S., Chan, B., Kramer, D., Sacherek, L., Olson, D.: Do batch and user evaluations give the same results? In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 17–24 (2000)

  279. 279.

    Hersh, W.R., Turpin, A., Sacherek, L., Olson, D., Price, S., Chan, B., Kraemer, D.: Further Analysis of whether batch and user evaluations give the same results with a question-answering task. In: Proceedings of the 9th Text REtrieval Conference (TREC 9) (2000)

  280. 280.

    Said, A.: Evaluating the accuracy and utility of recommender systems. PhD Thesis. Technische Universität Berlin (2013)

  281. 281.

    Turpin, A.H., Hersh, W.: Why batch and user evaluations do not give the same results. In: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 225–231 (2001)

  282. 282.

    Jannach, D., Lerche, L., Gedikli, F., Bonnin, G.: What recommenders recommend—an analysis of accuracy, popularity, and sales diversity effects. In: User Modeling, Adaptation, and Personalization, Springer, pp. 25–37 (2013)

  283. 283.

    Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Model. User-Adapt. Interact. 22(4–5), 441–504 (2012)

    Article  Google Scholar 

  284. 284.

    Jannach, D., Zanker, M., Ge, M., Gröning, M.: Recommender systems in computer science and information systems–a landscape of research. In: Proceedings of the 13th international conference, EC-Web, pp. 76–87 (2012)

  285. 285.

    Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the National Conference on Artificial Intelligence, pp. 439–446 (1999)

  286. 286.

    Palopoli, L., Rosaci, D., Sarné, G.M.: A multi-tiered recommender system architecture for supporting E-Commerce. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds.) Intelligent Distributed Computing VI, pp. 71–81. Springer (2013)

  287. 287.

    Palopoli, L., Rosaci, D., Sarné, G.M.: A multi-tiered recommender system architecture for supporting E-Commerce. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds.) Intelligent Distributed Computing VI, pp. 71–81. Springer (2013)

  288. 288.

    Lee, Y.-L., Huang, F.-H.: Recommender system architecture for adaptive green marketing. Expert Syst. Appl. 38(8), 9696–9703 (2011)

    Article  Google Scholar 

  289. 289.

    Prieto, M.E., Menéndez, V.H., Segura, A.A., Vidal, C.L.: A recommender system architecture for instructional engineering. In: Emerging Technologies and Information Systems for the Knowledge Society, Springer, pp. 314–321 (2008)

  290. 290.

    Bhatia, S., Caragea, C., Chen, H.-H., Wu, J., Treeratpituk, P., Wu, Z., Khabsa, M., Mitra, P., Giles, C.L.: Specialized research datasets in the CiteSeerx digital library. D-Lib Mag. 18(7/8) (2012)

  291. 291.

    Jack, K., Hristakeva, M., de Zuniga, R.G., Granitzer, M.: Mendeley’s open data for science and learning: a reply to the dataTEL challenge. Int. J. Technol. Enhanc. Learn. 4(1/2), 31–46 (2012)

    Article  Google Scholar 

  292. 292.

    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. Microsoft Research, MSR-TR-98-12 (1998)

  293. 293.

    Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: Proceedings of the 10th international conference on Information and knowledge management, pp. 247–254 (2001)

  294. 294.

    Casadevall, A., Fang, F.C.: Reproducible science. Infect. Immun. 78(12), 4972–4975 (2010)

    Article  Google Scholar 

  295. 295.

    Rehman, J.: Cancer research in crisis: are the drugs we count on based on bad science? http://www.salon.com/2013/09/01/is_cancer_research_facing_a_crisis/ (2013)

  296. 296.

    Drummond, C.: Replicability is not reproducibility: nor is it good science. In: Proceedings of the evaluation methods for MachineLearning Workshop at the 26th ICML (2009)

  297. 297.

    Al-Maskari, A., Sanderson, M., Clough, P.: The relationship between IR effectiveness measures and user satisfaction. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 773–774 (2007)

  298. 298.

    Knijnenburg, B.P., Willemsen, M.C., Kobsa, A.: A pragmatic procedure to support the user-centric evaluation of recommender systems. In: Proceedings of the 5th ACM conference on Recommender systems, pp. 321–324 (2011)

  299. 299.

    Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adapt. Interact. 22(4–5), 317–355 (2012)

    Article  Google Scholar 

  300. 300.

    Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adapt. Interact. 22(4–5), 317–355 (2012)

    Article  Google Scholar 

  301. 301.

    Ekstrand, M.D., Ludwig, M., Konstan, J.A., Riedl, J.T.: Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. In: Proceedings of the 5th ACM conference on Recommender systems, pp. 133–140 (2011)

  302. 302.

    Konstan, J.A., Adomavicius, G.: Toward identification and adoption of best practices in algorithmic recommender systems research. In: Proceedings of the international workshop on Reproducibility and replication in recommender systems evaluation, pp. 23–28 (2013)

  303. 303.

    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  304. 304.

    Perugini, S., Gonçalves, M.A., Fox, E.A.: Recommender systems research: a connection-centric survey. J. Intell. Inf. Syst. 23(2), 107–143 (2004)

    Article  MATH  Google Scholar 

  305. 305.

    Torre, I.: Adaptive systems in the era of the semantic and social web, a survey. User Model. User-Adapt. Interact. 19(5), 433–486 (2009)

    Article  Google Scholar 

  306. 306.

    Zanker, M., Jessenitschnig, M., Jannach, D., Gordea, S.: Comparing recommendation strategies in a commercial context. IEEE Intell. Syst. 22(3), 69–73 (2007)

    Article  Google Scholar 

  307. 307.

    Rich, E.: User modeling via stereotypes. Cogn. Sci. 3(4), 329–354 (1979)

    Article  Google Scholar 

  308. 308.

    Barla, M.: Towards social-based user modeling and personalization. Inf. Sci. Technol. Bull. ACM Slovakia 3, 52–60 (2011)

    Google Scholar 

  309. 309.

    Weber, I., Castillo, C.: The demographics of web search. In: Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 523–530 (2010)

  310. 310.

    Mattioli, D.: On Orbitz, Mac users steered to pricier hotels. Wall Str. J. vol. http://online.wsj.com/news/articles/SB10001424052702304458604577488822667325882 (2012)

  311. 311.

    Beel, J.: Towards effective research-paper recommender systems and user modeling based on mind maps. PhD Thesis. Otto-von-Guericke Universität Magdeburg (2015)

  312. 312.

    Beel, J., Langer, S., Kapitsaki, G.M., Breitinger, C., Gipp, B.: Exploring the potential of user modeling based on mind maps. In: Proceedings of the 23rd conference on User modelling, adaptation and personalization (UMAP) (to appear) (2015)

  313. 313.

    Beel, J., Gipp, B., Wilde, E.: Academic search engine optimization (ASEO): optimizing scholarly literature for Google Scholar and Co. J. Sch. Publ. 41(2), 176–190 (2010)

    Google Scholar 

  314. 314.

    Paik, W., Yilmazel, S., Brown, E., Poulin, M., Dubon, S., Amice, C.: Applying natural language processing (nlp) based metadata extraction to automatically acquire user preferences. In: Proceedings of the 1st international conference on Knowledge capture, pp. 116–122 (2001)

  315. 315.

    Seroussi, Y.: Utilising user texts to improve recommendations. In: De Bra, P., Kobsa, A., Chin, D. (eds.) User Modeling, Adaptation, and Personalization, pp. 403–406. Springer, Berlin (2010)

  316. 316.

    Seroussi, Y., Zukerman, I., Bohnert, F.: Collaborative inference of sentiments from texts. In: De Bra, P., Kobsa, A., Chin, D. (eds.) User Modeling, Adaptation, and Personalization, pp. 195–206. Springer, Berlin (2010)

  317. 317.

    Seroussi, Y., Zukerman, I., Bohnert, F.: Collaborative inference of sentiments from texts. In: De Bra, P., Kobsa, A., Chin, D. (eds.) User Modeling, Adaptation, and Personalization, pp. 195–206. Springer, Berlin (2010)

  318. 318.

    Esposito, F., Ferilli, S., Basile, T.M.A., Mauro, N.D.: Machine learning for digital document processing: from layout analysis to metadata extraction. Stud. Comput. Intell. (SCI) 90, 105–138 (2008)

    Google Scholar 

  319. 319.

    Shin, C.K., Doermann, D.: Classification of document page images based on visual similarity of layout structures. In: Proceedings of the SPIE document recognition and retrieval VII, pp. 182–190 (2000)

  320. 320.

    Buttler, D.: A short survey of document structure similarity algorithms. In: Proceedings of the 5th international conference on Internet computing (2004)

  321. 321.

    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  322. 322.

    McBryan, O.A.: GENVL and WWWW: tools for taming the Web. In: Proceedings of the 1st international World Wide Web conference, vol. 341 (1994)

  323. 323.

    Shi, S., Xing, F., Zhu, M., Nie, Z., Wen, J.-R.: Anchor text extraction for academic search. In: Proceedings of the 2009 workshop on Text and citation analysis for scholarly digital libraries (ACL-IJCNLP 2009), pp. 10–18 (2009)

  324. 324.

    Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval, Online edn. Cambridge University Press, Cambridge (2009)

    Google Scholar 

  325. 325.

    Councill, I.G., Giles, C.L., Kan, M.Y.: ParsCit: an open-source CRF reference string parsing package. Proc. LREC 2008, 661–667 (2008)

    Google Scholar 

  326. 326.

    Marinai, S.: Metadata extraction from PDF papers for digital library ingest. 10th international conference on Document analysis and recognition (2009)

  327. 327.

    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information Tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  328. 328.

    Brooks, T.A.: Private acts and public objects: an investigation of citer motivations. J. Am. Soc. Inf. Sci. 36(4), 223–229 (1985)

  329. 329.

    Liu, M.: Progress in documentation the complexities of citation practice: a review of citation studies. J. Documentation 49(4), 370–408 (1993)

    Article  Google Scholar 

  330. 330.

    MacRoberts, M.H., MacRoberts, B.: Problems of citation analysis. Scientometrics 36, 435–444 (1996)

    Article  Google Scholar 

  331. 331.

    Sosnovsky, S., Dicheva, D.: Ontological technologies for user modeling. Int. J. Metadata Semant. Ontol. 5(1), 32–71 (2010)

    Article  Google Scholar 

  332. 332.

    Sundar, S.S., Oeldorf-Hirsch, A., Xu, Q.: The bandwagon effect of collaborative filtering technology. In: CHI’08 extended abstracts on Human factors in computing systems, pp. 3453–3458 (2008)

  333. 333.

    Mehta, B., Hofmann, T., Fankhauser, P.: Lies and propaganda: detecting spam users in collaborative filtering. In: Proceedings of the 12th international conference on Intelligent user interfaces, pp. 14–21 (2007)

  334. 334.

    Mehta, B., Hofmann, T., Nejdl, W.: Robust collaborative filtering. In: Proceedings of the 2007 ACM conference on Recommender systems, pp. 49–56 (2007)

  335. 335.

    Mehta, B., Nejdl, W.: Attack resistant collaborative filtering. In: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 75–82 (2008)

  336. 336.

    Sugiyama, K., Kan, M.Y.: Serendipitous recommendation for scholarly papers considering relations among researchers. In: Proceeding of the 11th annual international ACM/IEEE joint conference on Digital libraries, pp. 307–310 (2011)

  337. 337.

    Burke, R.: Hybrid web recommender systems. The adaptive web, pp. 377–408 (2007)

  338. 338.

    Ahlgren, P., Colliander, C.: Document-document similarity approaches and science mapping: experimental comparison of five approaches. J. Informetr. 3(1), 49–63 (2009)

    Article  Google Scholar 

  339. 339.

    Hammouda, K.M., Kamel, M.S.: Phrase-based document similarity based on an index graph model. In: Data mining, 2002. ICDM 2003. Proceedings. 2002 IEEE international conference on, pp. 203–210 (2002)

  340. 340.

    Lee, M.D., Pincombe, B., Welsh, M.: An empirical evaluation of models of text document similarity. In: Proceedings of the 27th annual conference of the Cognitive Science Society, pp. 1254–1259 (2005)

  341. 341.

    Tsymbal, A.: The Problem of Concept Drift: Definitions and Related Work. Computer Science Department, Trinity College, Dublin (2004)

    Google Scholar 

  342. 342.

    Victor, P., De Cock, M., Cornelis, C.: Trust and recommendations. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. B. (eds.) Recommender Systems Handbook, pp. 645–675. Springer (2011)

  343. 343.

    Verbert, K., Parra, D., Brusilovsky, P., Duval, E.: Visualizing recommendations to support exploration, transparency and controllability. In: Proceedings of the 2013 international conference on Intelligent user interfaces, pp. 351–362 (2013)

  344. 344.

    Lam, S., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. Emerging Trends in Information and Communication Security, pp. 14–29 (2006)

  345. 345.

    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web, pp. 22–32 (2005)

  346. 346.

    Burke, R., Ramezani, M.: Matching recommendation technologies and domains. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 367–386. Springer (2011)

  347. 347.

    Zhang, Y., Callan, J., Minka, T.: Novelty and redundancy detection in adaptive filtering. In: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 81–88 (2002)

  348. 348.

    Pizzato, L., Rej, T., Yacef, K., Koprinska, I., Kay, J.: Finding someone you will like and who won’t reject you In: A. Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) User Modeling, Adaption and Personalization, pp. 269–280. Springer, Berlin (2011)

  349. 349.

    Cosley, D., Lam, S.K., Albert, I., Konstan, J.A., Riedl, J.: Is seeing believing? How recommender system interfaces affect users’ opinions. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 585–592 (2003)

  350. 350.

    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM conference on Computer supported cooperative work, pp. 241–250 (2000)

  351. 351.

    Carmagnola, F., Cena, F., Gena, C.: User model interoperability: a survey. User Model. User-Adapt. Interact. 21(3), 285–331 (2011)

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Joeran Beel.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Beel, J., Gipp, B., Langer, S. et al. Research-paper recommender systems: a literature survey. Int J Digit Libr 17, 305–338 (2016). https://doi.org/10.1007/s00799-015-0156-0

Download citation

Keywords

  • Recommender system
  • User modeling
  • Research paper recommender systems
  • Content based filtering
  • Review
  • Survey