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International Conference on Electronic Commerce and Web Technologies

EC-Web 2011: E-Commerce and Web Technologies pp 172–183Cite as

Information Retrieval and Folksonomies together for Recommender Systems

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 85))

Abstract

The powerful and democratic activity of social tagging allows the wide set of Web users to add free annotations on resources. Tags express user interests, preferences and needs, but also automatically generate folksonomies. They can be considered as gold mine, especially for e-commerce applications, in order to provide effective recommendations. Thus, several recommender systems exploit folksonomies in this context. Folksonomies have also been involved in many information retrieval approaches. In considering that information retrieval and recommender systems are siblings, we notice that few works deal with the integration of their approaches, concepts and techniques to improve recommendation. This paper is a first attempt in this direction. We propose a trail through recommender systems, social Web, e-commerce and social commerce, tags and information retrieval: an overview on the methodologies, and a survey on folksonomy-based information retrieval from recommender systems point of view, delineating a set of open and new perspectives.

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References

  1. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook, 1st edn. Hardcover (2011)

    Google Scholar 

  2. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G. (eds.): Recommender Systems An Introduction. Hardback (November 2010)

    Google Scholar 

  3. Dattolo, A., Ferrara, F., Tasso, C.: On social semantic relations for recommending tags and resources using folksonomies. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human-Computer Systems Interaction. Backgrounds and Applications, vol. 2. Springer, Heidelberg (in press)

    Google Scholar 

  4. Kumar, A., Thambidurai, P.: Collaborative web recommendation systems a survey approach. Global Journal of Computer Science and Technology 9(5), 30–36 (2010)

    Google Scholar 

  5. Schafer, B.J., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: ACM Conference on Electronic Commerce, pp. 158–166 (1999)

    Google Scholar 

  6. Zimmermann, H.D.: From eCommerce to eCommerce 2.0: The Changing Role of the Customer. In: Antlová, K. (ed.) Proceedings of the Liberec Informatics Forum, November 4-5, pp. 171–179 (2010)

    Google Scholar 

  7. Burke, R.: The adaptive web, pp. 377–408. Springer, Heidelberg (2007)

    Book  Google Scholar 

  8. Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A., Cohen, M.D.: Intelligent information-sharing systems. Commun. ACM 30, 390–402 (1987)

    Article  Google Scholar 

  9. Ramezani, M., Bergman, L., Thompson, R., Burke, R., Mobasher, B.: Selecting and applying recommendation technology. In: International Workshop on Recommendation and Collaboration in Conjunction with 2008 International ACM Conference on Intelligent User Interfaces, IUI 2008 (2008)

    Google Scholar 

  10. Salton, G.: Automatic Information Organization and Retrieval. McGraw Hill Text, New York (1968)

    Google Scholar 

  11. Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)

    Book  Google Scholar 

  12. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)

    Article  Google Scholar 

  13. Robertson, S.E.: The probabilistic character of relevance. Inf. Process. Manage. 13(4), 247–251 (1977)

    Article  Google Scholar 

  14. Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 275–281. ACM, New York (1998)

    Google Scholar 

  15. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)

    Article  Google Scholar 

  16. Robertson, S.E., Walker, S., Hancock-Beaulieu, M., Willet, P.: Okapi at TREC-7: Automatic ad hoc, filtering, VLC and interactive track. In: TREC-7: The 7th Text REtrieval Conference, National Institute of Standards and Technology (NIST), pp. 253–264 (1998)

    Google Scholar 

  17. Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35, 29–38 (1992)

    Article  Google Scholar 

  18. Jiang, F., Wang, Z.: Pagerank-based collaborative filtering recommendation. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. LNCS, vol. 6377, pp. 597–604. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  19. Jeon, H., Kim, T., Choi, J.: Personalized information retrieval by using adaptive user profiling and collaborative filtering. AISS 2(4), 134–142 (2010)

    Article  Google Scholar 

  20. Linda, ling Lai, S.: Social commerce: e-commerce in social media context. World Academy of Science, Engineering and Technology, 39–44 (2010)

    Google Scholar 

  21. Weisberg, J., Te’eni, D., Russo, M.L.A.: Past purchase and intention to purchase in e-commerce: the mediation of social presence and trust. Internet Research 21(1) (2011)

    Google Scholar 

  22. Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. J. Inf. Sci. 32, 198–208 (2006)

    Article  Google Scholar 

  23. Milicevic, A., Nanopoulos, A., Ivanovic, M.: Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review 33, 187–209 (2010), 10.1007/s10462-009-9153-2

    Article  Google Scholar 

  24. Siersdorfer, S., Sizov, S.: Social recommender systems for web 2.0 folksonomies. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT 2009, pp. 261–270. ACM, New York (2009)

    Chapter  Google Scholar 

  25. Xia, X., Zhang, S., Li, X.: A personalized recommendation model based on social tags. In: DBTA 2010, pp. 1–5 (2010)

    Google Scholar 

  26. Rendle, S., Lars, S.T.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 81–90. ACM, New York (2010)

    Google Scholar 

  27. Niwa, S., Doi, T., Honiden, S.: Web page recommender system based on folksonomy mining for itng 2006 submissions. In: Proceedings of the Third International Conference on Information Technology: New Generations, pp. 388–393. IEEE Computer Society, Washington, DC (2006)

    Google Scholar 

  28. Durao, F., Dolog, P.: A personalized tag-based recommendation in social web systems. Adaptation and Personalization for Web 2.0, 40 (2009)

    Google Scholar 

  29. Wetzker, R., Zimmermann, C., Bauckhage, C., Albayrak, S.: I tag, you tag: translating tags for advanced user models. In: WSDM 2010, pp. 71–80 (2010)

    Google Scholar 

  30. Carmagnola, F., Cena, F., Cortassa, O., Gena, C., Torre, I.: Towards a tag-based user model: How can user model benefit from tags? In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 445–449. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  31. Simpson, E., Butler, M.H.: Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, IGI Global, pp. 43–64 (2009)

    Google Scholar 

  32. Jiao, Y., Cao, G.: A collaborative tagging system for personalized recommendation in b2c electronic commerce. In: International Conference on Wireless Communications, Networking and Mobile Computing, WiCom 2007, pp. 3609–3612 (September 2007)

    Google Scholar 

  33. Hassan-Montero, Y., Herrero-Solana, V.: Improving tag-clouds as visual information retrieval interfaces. In: InScit 2006: International Conference on Multidisciplinary Information Sciences and Technologies (2006)

    Google Scholar 

  34. Bar-Ilan, J., Zhitomirsky-Geffet, M., Miller, Y., Shoham, S.: Tag, cloud and ontology based retrieval of images. In: Proceeding of the Third Symposium on Information Interaction in Context, IIiX 2010, pp. 85–94. ACM, New York (2010)

    Google Scholar 

  35. Knautz, K., Soubusta, S., Stock, W.G.: Tag clusters as information retrieval interfaces. In: Proceedings of the 2010 43rd Hawaii International Conference on System Sciences, HICSS 2010, pp. 1–10. IEEE Computer Society, Washington, DC (2010)

    Chapter  Google Scholar 

  36. Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: Search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  37. Liu, D., Hua, X.S., Wang, M., Zhang, H.: Boost search relevance for tag-based social image retrieval. In: Proceedings of the 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, pp. 1636–1639. IEEE Press, Piscataway (2009)

    Chapter  Google Scholar 

  38. Bender, M., Crecelius, T., Kacimi, M., Michel, S., Neumann, T., Parreira, J.X., Schenkel, R., Weikum, G.: Exploiting social relations for query expansion and result ranking. In: Data Engineering for Blogs, Social Media, and Web 2.0, ICDE 2008 Workshops, pp. 501–506 (2008)

    Google Scholar 

  39. Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 501–510. ACM, New York (2007)

    Google Scholar 

  40. Wang, J., Davison, B.D.: Explorations in tag suggestion and query expansion. In: Proceeding of the 2008 ACM Workshop on Search in Social Media, SSM 2008, pp. 43–50. ACM, New York (2008)

    Chapter  Google Scholar 

  41. Biancalana, C., Micarelli, A.: Social tagging in query expansion: A new way for personalized web search. In: Proceedings IEEE CSE 2009, 12th IEEE International Conference on Computational Science and Engineering, August 29-31, pp. 1060–1065. IEEE Computer Society, Vancouver (2009)

    Chapter  Google Scholar 

  42. Jin, S., Lin, H., Su, S.: Query expansion based on folksonomy tag co-occurrence analysis. In: 2009 IEEE International Conference on Granular Computing, pp. 300–305. IEEE, Los Alamitos (2009)

    Chapter  Google Scholar 

  43. Bellogín, A., Castells, P.: A performance prediction approach to enhance collaborative filtering performance. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 382–393. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  44. Gemmell, J., Schimoler, T., Mobasher, B., Burke, R.D.: Resource recommendation in collaborative tagging applications. In: Buccafurri, F., Semeraro, G. (eds.) EC-Web 2010. LNBIP, vol. 61, pp. 1–12. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  45. Costa, A., Roda, F.: Recommender systems by means of information retrieval. CoRR (a more recent version of this paper will be published in WIMS 2011) abs/1008.4815 (2010)

    Google Scholar 

  46. Aciar, S., Herrera, J.L., de la Rosa, J.L.: Integrating information sources for recommender systems. In: CCIA 2005, pp. 421–428 (2005)

    Google Scholar 

  47. Gravano, L., García-Molina, H., Tomasic, A.: Gloss: text-source discovery over the internet. ACM Trans. Database Syst. 24, 229–264 (1999)

    Article  Google Scholar 

  48. Kim, J., Croft, W.B.: Ranking using multiple document types in desktop search. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 50–57. ACM, New York (2010)

    Google Scholar 

  49. Vallet, D., Cantador, I., Jose, J.: Personalizing Web Search with Folksonomy-Based User and Document Profiles. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 420–431. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  50. Lu, C., Hu, X., Chen, X., Park, J.R., He, T., Li, Z.: The topic-perspective model for social tagging systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 683–692. ACM, New York (2010)

    Google Scholar 

  51. Peters, I., Stock, W.G.: Power tags in information retrieval. Library Hi Tech. 28(1), 81–93 (2010)

    Article  Google Scholar 

  52. Shen, X., Tan, B., Zhai, C.: Implicit user modeling for personalized search. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM 2005, pp. 824–831. ACM, New York (2005)

    Google Scholar 

  53. Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing cold-start problem in recommendation systems. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, ICUIMC 2008, pp. 208–211. ACM, New York (2008)

    Google Scholar 

  54. Wei, H., Xin, C., Haibo, W.: Product information retrieval based on opinion mining. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 5, pp. 2489–2492 (August 2010)

    Google Scholar 

  55. Zhang, L., Zhu, M., Huang, W.: A Framework for an Ontology-based E-commerce Product Information Retrieval System. Journal of Computers 4(6), 436–443 (2009)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. IRIT, Université P. Sabatier, 118 rte de Narbonne, F-31062, Toulouse, France

    Max Chevalier & Gilles Hubert

  2. University of Udine, Via delle Scienze 206, I-33100, Udine, Italy

    Antonina Dattolo & Emanuela Pitassi

Authors
  1. Max Chevalier
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  2. Antonina Dattolo
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  3. Gilles Hubert
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  4. Emanuela Pitassi
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Editor information

Editors and Affiliations

  1. Institute of Software Technology and Interactive Systems, Business Informatics Group (BIG), Vienna University of Technology, Favoritenstrasse 9 - 11 / 188-3, 1040, Vienna, Austria

    Christian Huemer

  2. Institute of Computer Science, Decision Sciences and Systems (DSS), Technical University Munich, Boltzmannstr. 3, 85748, Garching, Germany

    Thomas Setzer

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Chevalier, M., Dattolo, A., Hubert, G., Pitassi, E. (2011). Information Retrieval and Folksonomies together for Recommender Systems. In: Huemer, C., Setzer, T. (eds) E-Commerce and Web Technologies. EC-Web 2011. Lecture Notes in Business Information Processing, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23014-1_15

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  • DOI: https://doi.org/10.1007/978-3-642-23014-1_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23013-4

  • Online ISBN: 978-3-642-23014-1

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