Novelty and Diversity in Recommender Systems

Abstract

Novelty and diversity have been identified, along with accuracy, as foremost properties of useful recommendations. Considerable progress has been made in the field in terms of the definition of methods to enhance such properties, as well as methodologies and metrics to assess how well such methods work. In this chapter we give an overview of the main contributions to this area in the field of recommender systems, and seek to relate them together in a unified view, analyzing the common elements underneath the different forms under which novelty and diversity have been addressed, and identifying connections to closely related work on diversity in other fields.

References

  1. 1.
    Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: Or how to better expect the unexpected. ACM Transactions on Intelligent Systems and Technology 4(5), Special Section on Novelty and Diversity in Recommender Systems, 54:1–54-32 (2015)Google Scholar
  2. 2.
    Adomavicius, G., Kwon, Y.: Maximizing aggregate recommendation diversity: A graph-theoretic approach. In: Proceedings of the 1st ACM RecSys Workshop on Novelty and Diversity in Recommender Systems, DiveRS 2011, pp. 3–10 (2011)Google Scholar
  3. 3.
    Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering 24(5), 896–911 (2012)CrossRefGoogle Scholar
  4. 4.
    Adomavicius, G., Kwon, Y.: Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS Journal on Computing 26(2), 351–369 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  6. 6.
    Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Proceedings of the 2nd ACM Conference on Web Search and Data Mining, WSDM 2009, pp. 5–14. ACM, New York, NY, USA (2009)Google Scholar
  7. 7.
    Allan, J., Wade, C., Bolivar, A.: Retrieval and novelty detection at the sentence level. In: Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003, pp. 314–321. ACM, New York, NY, USA (2003)Google Scholar
  8. 8.
    Alodhaibi, K., Brodsky, A., Mihaila, G.A.: COD: Iterative utility elicitation for diversified composite recommendations. In: Proceedings of the 43rd Hawaii International Conference on System Sciences, HICSS 2010, pp. 1–10. IEEE Computer Society, Washington, DC, USA (2010)Google Scholar
  9. 9.
    Anderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion (2006)Google Scholar
  10. 10.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval, 2nd edn. Addison-Wesley Publishing Company, USA (2008)Google Scholar
  11. 11.
    Bellogín, A., Cantador, I., Castells, P.: A comparative study of heterogeneous item recommendations in social systems. Information Sciences 221, 142–169 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Bessa, A., Veloso, A., Ziviani, N.: Using mutual influence to improve recommendations. In: O. Kurland, M. Lewenstein, E. Porat (eds.) String Processing and Information Retrieval, Lecture Notes in Computer Science, vol. 8214, pp. 17–28. Springer International Publishing (2013)Google Scholar
  13. 13.
    Bhandari, U., Sugiyama, K., Datta, A., Jindal, R.: Serendipitous recommendation for mobile apps using item-item similarity graph. In: R.E. Banchs, F. Silvestri, T.Y. Liu, M. Zhang, S. Gao, J. Lang (eds.) Information Retrieval Technology, Lecture Notes in Computer Science, vol. 8281, pp. 440–451. Springer Berlin Heidelberg (2013)Google Scholar
  14. 14.
    Boim, R., Milo, T., Novgorodov, S.: Diversification and refinement in collaborative filtering recommender. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, pp. 739–744. ACM, New York, NY, USA (2011)Google Scholar
  15. 15.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, UAI 1998, pp. 43–52. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1998)Google Scholar
  16. 16.
    Brickman, P., D’Amato, B.: Exposure effects in a free-choice situation. Journal of Personality and Social Psychology 32(3), 415–420 (1975)CrossRefGoogle Scholar
  17. 17.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 335–336. ACM, New York, NY, USA (1998)Google Scholar
  18. 18.
    Carterette, B.: System effectiveness, user models, and user utility: A conceptual framework for investigation. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 903–912. ACM, New York, NY, USA (2011)Google Scholar
  19. 19.
    Castagnos, S., Brun, A., Boyer, A.: When diversity is needed but not expected! In: Proceedings of the 3rd International Conference on Advances in Information Mining and Management, IMMM 2013, pp. 44–50. IARIA, Lisbon, Portugal (2013)Google Scholar
  20. 20.
    Celma, O., Herrera, P.: A new approach to evaluating novel recommendations. In: Proceedings of the 2nd ACM Conference on Recommender Systems, RecSys 2008, pp. 179–186. ACM, New York, NY, USA (2008)Google Scholar
  21. 21.
    Chapelle, O., Ji, S., Liao, C., Velipasaoglu, E., Lai, L., Wu, S.L.: Intent-based diversification of web search results: Metrics and algorithms. Information Retrieval 14(6), 572–592 (2011)CrossRefGoogle Scholar
  22. 22.
    Chen, H., Karger, D.R.: Less is more: Probabilistic models for retrieving fewer relevant documents. In: Proceedings of the 2nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 429–436. ACM, New York, NY, USA (2006)Google Scholar
  23. 23.
    Clarke, C.L., Craswell, N., Soboroff, I., Cor: Overview of the TREC 2010 web track. In: Proceedings of the 19th Text REtrieval Conference, TREC 2010. National Institute of Standards and Technology (NIST) (2010)Google Scholar
  24. 24.
    Clarke, C.L., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 659–666. ACM, New York, NY, USA (2008)Google Scholar
  25. 25.
    Coombs, C., Avrunin, G.S.: Single peaked preference functions and theory of preference. Psychological Review 84(2), 216–230 (1977)CrossRefGoogle Scholar
  26. 26.
    Deselaers, T., Gass, T., Dreuw, P., Ney, H.: Jointly optimising relevance and diversity in image retrieval. In: Proceedings of the ACM International Conference on Image and Video Retrieval, CIVR 2009, pp. 39:1–39:8. ACM, New York, NY, USA (2009)Google Scholar
  27. 27.
    Drosou, M., Pitoura, E.: Comparing diversity heuristics. Tech. rep., Technical Report 2009–05. Computer Science Department, University of Ioannina (2009)Google Scholar
  28. 28.
    Drosou, M., Pitoura, E.: Diversity over continuous data. IEEE Data Engineering Bulleting 32(4), 49–56 (2009)Google Scholar
  29. 29.
    Drosou, M., Pitoura, E.: Disc diversity: Result diversification based on dissimilarity and coverage. Proceedings of the VLDB Endowment 6(1), 13–24 (2012)CrossRefGoogle Scholar
  30. 30.
    Drosou, M., Stefanidis, K., Pitoura, E.: Preference-aware publish/subscribe delivery with diversity. In: Proceedings of the 3rd ACM Conference on Distributed Event-Based Systems, DEBS 2009, pp. 6:1–6:12. ACM, New York, NY, USA (2009)Google Scholar
  31. 31.
    Fleder, D.M., Hosanagar, K.: Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Science 55(5), 697–712 (2009)CrossRefGoogle Scholar
  32. 32.
    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, RecSys 2010, pp. 257–260. ACM, New York, NY, USA (2010)Google Scholar
  33. 33.
    Ge, M., Jannach, D., Gedikli, F., Hepp, M.: Effects of the placement of diverse items in recommendation lists. In: Proceedings of the 14th International Conference on Enterprise Information Systems, ICEIS 2012, pp. 201–208. SciTePress (2012)Google Scholar
  34. 34.
    He, J., Meij, E., de Rijke, M.: Result diversification based on query-specific cluster ranking. Journal of the Association for Information Science and Technology 62(3), 550–571 (2011)Google Scholar
  35. 35.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999, pp. 230–237. ACM, New York, NY, USA (1999)Google Scholar
  36. 36.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  37. 37.
    Hu, R., Pu, P.: Enhancing recommendation diversity with organization interfaces. In: Proceedings of the 16th International Conference on Intelligent User Interfaces, IUI 2011, pp. 347–350. ACM, New York, NY, USA (2011)Google Scholar
  38. 38.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE Computer Society, Washington, DC, USA (2008)Google Scholar
  39. 39.
    Hurley, N., Zhang, M.: Novelty and diversity in top-N recommendation – analysis and evaluation. ACM Transactions on Internet Technology 10(4), 14:1–14:30 (2011)Google Scholar
  40. 40.
    Hurley, N.J.: Personalised ranking with diversity. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 379–382. ACM, New York, NY, USA (2013)Google Scholar
  41. 41.
    Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G., Filannino, M., Molino, P.: Introducing serendipity in a content-based recommender system. In: Proceedings of the 8th Conference on Hybrid Intelligent Systems, HIS 2008, pp. 168–173. IEEE (2008)Google Scholar
  42. 42.
    Jannach, D., Lerche, L., Gedikli, G., Bonnin, G.: What recommenders recommend - an analysis of accuracy, popularity, and sales diversity effects. In: Proceedings of the 21st International Conference on User Modeling, Adaptation and Personalization, pp. 25–37. Springer (2013)Google Scholar
  43. 43.
    Jeuland, A.P.: Brand preference over time: A partially deterministic operationalization of the notion of variety seeking. In: Proceedings of the Educators’ Conference, 43, pp. 33–37. American Marketing Association (1978)Google Scholar
  44. 44.
    Kahn, B.E.: Consumer variety-seeking among goods and services: An integrative review. Journal of Intelligent Information Systems 2(3), 139–148 (1995)Google Scholar
  45. 45.
    Kwon, Y.: Improving neighborhood-based CF systems: Towards more accurate and diverse recommendations. Journal of Intelligent Information Systems 18(3), 119–135 (2012)Google Scholar
  46. 46.
    Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 210–217. ACM, New York, NY, USA (2010)Google Scholar
  47. 47.
    Levinson, D.: Ethnic Groups Worldwide: A ready Reference Handbook. Oryx Press (1998)Google Scholar
  48. 48.
    Li, X., Murata, T.: Multidimensional clustering based collaborative filtering approach for diversified recommendation. In: Proceedings of the 7th International Conference on Computer Science & Education, ICCSE 2012, pp. 905–910. IEEE (2012)Google Scholar
  49. 49.
    Lubatkin, M., Chatterjee, S.: Extending modern portfolio theory into the domain of corporate diversification: Does it apply? The Academy of Management Journal 37(1), 109–136 (1994)CrossRefGoogle Scholar
  50. 50.
    Maddi, S.R.: The pursuit of consistency and variety. In: R.P. Abelson, E. Aronson, W.J. McGuire, T.M. Newcomb, M.J. Rosenberg, P.H. Tannenbaum (eds.) Theories of Cognitive Consistency: A Sourcebook. Rand McNally (1968)Google Scholar
  51. 51.
    McAlister, L., Pessemier, E.A.: Variety seeking behaviour: and interdisciplinary review. Journal of Consumer Research 9(3), 311–322 (1982)CrossRefGoogle Scholar
  52. 52.
    McGinty, L., Smyth, B.: On the role of diversity in conversational recommender systems. In: Proceedings of the 5th International Conference on Case-based Reasoning, ICCBR 2003, pp. 276–290. Springer-Verlag, Berlin, Heidelberg (2003)Google Scholar
  53. 53.
    McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems, CHI EA 2006, pp. 1097–1101. ACM, New York, NY, USA (2006)Google Scholar
  54. 54.
    McSherry, D.: Diversity-conscious retrieval. In: S. Craw, A. Preece (eds.) Advances in Case-Based Reasoning, Lecture Notes in Computer Science, vol. 2416, pp. 219–233. Springer Berlin Heidelberg (2002)CrossRefGoogle Scholar
  55. 55.
    Moffat, A., Zobel, J.: Rank-biased precision for measurement of retrieval effectiveness. ACM Transactions on Information Systems 27(1), 2:1–2:27 (2008)Google Scholar
  56. 56.
    Mourão, F., Fonseca, C., Araújo, C., Meira, W.: The oblivion problem: Exploiting forgotten items to improve recommendation diversity. In: Proceedings of the 1st ACM RecSys Workshop on Novelty and Diversity in Recommender System, DiveRS 2011 (2011)Google Scholar
  57. 57.
    Murakami, T., Mori, K., Orihara, R.: Metrics for evaluating the serendipity of recommendation lists. In: K. Satoh, A. Inokuchi, K. Nagao, T. Kawamura (eds.) New Frontiers in Artificial Intelligence, Lecture Notes in Computer Science, vol. 4914, pp. 40–46. Springer Berlin Heidelberg (2008)CrossRefGoogle Scholar
  58. 58.
    Niemann, K., Wolpers, M.: A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In: Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 955–963. ACM, New York, NY, USA (2013)Google Scholar
  59. 59.
    Oku, K., Hattori, F.: Fusion-based recommender system for improving serendipity. In: Proceedings of the 1st ACM RecSys Workshop on Novelty and Diversity in Recommender Systems, DiveRS 2011 (2011)Google Scholar
  60. 60.
    Pariser, E.: The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin Books (2012)Google Scholar
  61. 61.
    Park, Y.J., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: Proceedings of the 2th ACM Conference on Recommender Systems, RecSys 2008, pp. 11–18. ACM, New York, NY, USA (2008)Google Scholar
  62. 62.
    Patil, G.P., Taillie, C.: Diversity as a concept and its measurement. Journal of the American Statistical Association 77(379), 548–561 (1982)MATHMathSciNetCrossRefGoogle Scholar
  63. 63.
    Radlinski, F., Dumais, S.: Improving personalized web search using result diversification. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 691–692. ACM, New York, NY, USA (2006)Google Scholar
  64. 64.
    Raju, P.S.: Optimum stimulation level: Its relationship to personality, demographics and exploratory behavior. Journal of Consumer Research 7(3), 272–282 (1980)CrossRefGoogle Scholar
  65. 65.
    Rao, J., Jia, A., Feng, Y., Zhao, D.: Taxonomy based personalized news recommendation: Novelty and diversity. In: X. Lin, Y. Manolopoulos, D. Srivastava, G. Huang (eds.) Web Information Systems Engineering – WISE 2013, Lecture Notes in Computer Science, vol. 8180, pp. 209–218. Springer Berlin Heidelberg (2013)CrossRefGoogle Scholar
  66. 66.
    Ribeiro, M.T., Lacerda, A., Veloso, A., Ziviani, N.: Pareto-efficient hybridization for multi-objective recommender systems. In: Proceedings of the 6th ACM Conference on Recommender Systems, RecSys 2012, pp. 19–26. ACM, New York, NY, USA (2012)Google Scholar
  67. 67.
    Santos, R.L., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 881–890. ACM, New York, NY, USA (2010)Google Scholar
  68. 68.
    Schafer, J.B., Konstan, J.A., Riedl, J.: Meta-recommendation systems: User-controlled integration of diverse recommendations. In: Proceedings of the 11th ACM Conference on Information and Knowledge Management, CIKM 2002, pp. 43–51. ACM, New York, NY, USA (2002)Google Scholar
  69. 69.
    Shi, L.: Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 57–64. ACM, New York, NY, USA (2013)Google Scholar
  70. 70.
    Smyth, B., McClave, P.: Similarity vs. diversity. In: Proceedings of the 4th International Conference on Case-Based Reasoning, ICCBR 2001, pp. 347–361. Springer-Verlag, London, UK, UK (2001)Google Scholar
  71. 71.
    Su, R., Yin, L., Chen, K., Yu, Y.: Set-oriented personalized ranking for diversified top-N recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 415–418. ACM, New York, NY, USA (2013)Google Scholar
  72. 72.
    Szlávik, Z., Kowalczyk, W., Schut, M.: Diversity measurement of recommender systems under different user choice models. In: Proceedings of the 5th AAAI Conference on Weblogs and Social Media, ICWSM 2011. The AAAI Press (2011)Google Scholar
  73. 73.
    Taramigkou, M., Bothos, E., Christidis, K., Apostolou, D., Mentzas, G.: Escape the bubble: Guided exploration of music preferences for serendipity and novelty. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 335–338. ACM, New York, NY, USA (2013)Google Scholar
  74. 74.
    Vallet, D., Castells, P.: Personalized diversification of search results. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 841–850. ACM, New York, NY, USA (2012)Google Scholar
  75. 75.
    Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the 5th ACM Conference on Recommender Systems, RecSys 2011, pp. 109–116. ACM, New York, NY, USA (2011)Google Scholar
  76. 76.
    Vargas, S., Castells, P.: Improving sales diversity by recommending users to items. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014, pp. 145–152. ACM, New York, NY, USA (2014)Google Scholar
  77. 77.
    Vargas, S., Castells, P., Vallet, D.: Intent-oriented diversity in recommender systems. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 1211–1212. ACM, New York, NY, USA (2011)Google Scholar
  78. 78.
    Vargas, S., Castells, P., Vallet, D.: Explicit relevance models in intent-oriented information retrieval diversification. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 75–84. ACM, New York, NY, USA (2012)Google Scholar
  79. 79.
    Ribeiro, M.T., Ziviani, N., Silva De Moura, E., Hata, I., Lacerda, A., Veloso, A.: Multiobjective pareto-efficient approaches for recommender systems. ACM Transactions on Intelligent Systems and Technology 4(5), Special Section on Novelty and Diversity in Recommender Systems, 53:1–53-20 (2015)Google Scholar
  80. 80.
    Wang, J.: Mean-variance analysis: A new document ranking theory in information retrieval. In: Proceedings of the 31th European Conference on Information Retrieval, ECIR 2009, pp. 4–16. Springer-Verlag, Berlin, Heidelberg (2009)Google Scholar
  81. 81.
    Welch, M.J., Cho, J., Olston, C.: Search result diversity for informational queries. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 237–246. ACM, New York, NY, USA (2011)Google Scholar
  82. 82.
    Wu, Q., Tang, F., Li, L., Barolli, L., You, I., Luo, Y., Li, H.: Recommendation of more interests based on collaborative filtering. In: Proceedings of the 26 IEEE Conference on Advanced Information Networking and Applications, AINA 2012, pp. 191–198. IEEE (2012)Google Scholar
  83. 83.
    Yu, C., Lakshmanan, L., Amer-Yahia, S.: It takes variety to make a world: Diversification in recommender systems. In: Proceedings of the 12th International Conference on Extending Database Technology, EDBT 2009, pp. 368–378. ACM, New York, NY, USA (2009)Google Scholar
  84. 84.
    Zhai, C.X., Cohen, W.W., Lafferty, J.: Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003, pp. 10–17. ACM, New York, NY, USA (2003)Google Scholar
  85. 85.
    Zhang, M., Hurley, N.: Avoiding monotony: Improving the diversity of recommendation lists. In: Proceedings of the 2nd ACM Conference on Recommender Systems, RecSys 2008, pp. 123–130. ACM, New York, NY, USA (2008)Google Scholar
  86. 86.
    Zhang, M., Hurley, N.: Novel item recommendation by user profile partitioning. In: Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2009, pp. 508–515. IEEE Computer Society, Washington, DC, USA (2009)Google Scholar
  87. 87.
    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, SIGIR 2002, pp. 81–88. ACM, New York, NY, USA (2002)Google Scholar
  88. 88.
    Zhang, Y.C., Séaghdha, D.O., Quercia, D., Jambor, T.: Auralist: Introducing serendipity into music recommendation. In: Proceedings of the 5th ACM Conference on Web Search and Data Mining, WSDM 2012, pp. 13–22. ACM, New York, NY, USA (2012)Google Scholar
  89. 89.
    Zhou, T., Kuscsik, Z., Liu, J.G., Medo, M., Wakeling, J.R., Zhang, Y.C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences 107(10), 4511–4515 (2010)CrossRefGoogle Scholar
  90. 90.
    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, WWW 2005, pp. 22–32. ACM, New York, NY, USA (2005)Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Universidad Autonoma de MadridMadridSpain
  2. 2.University College DublinDublinIreland

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