A Class-Based Strategy to User Behavior Modeling in Recommender Systems

Part of the Studies in Computational Intelligence book series (SCI, volume 647)


A recommender system is a tool employed to filter the huge amounts of data that companies have to deal with, and produce effective suggestions to the users. The estimation of the interest of a user toward an item, however, is usually performed at the level of a single item, i.e., for each item not evaluated by a user, canonical approaches look for the rating given by similar users for that item, or for an item with similar content. Such approach leads toward the so-called overspecialization/serendipity problem, in which the recommended items are trivial and users do not come across surprising items. This work first shows that user preferences are actually distributed over a small set of classes of items, leading the recommended items to be too similar to the ones already evaluated, then we propose a novel model, named Class Path Information (CPI), able to represent the current and future preferences of the users in terms of a ranked set of classes of items. The proposed approach is based on a semantic analysis of the items evaluated by the users, in order to extend their ground truth and infer the future preferences. The performed experiments show that our approach, by including in the CPI model the same classes predicted by a state-of-the-art recommender system, is able to accurately model the user preferences in terms of classes, instead of in terms of single items, allowing to recommend non trivial items.


  1. 1.
    Abbar, S., Amer-Yahia, S., Indyk, P., Mahabadi, S.: Real-time recommendation of diverse related articles. In: Schwabe, D., Almeida, V.A.F., Glaser, H., Baeza-Yates, R.A., Moon, S.B. (eds.) 22nd International World Wide Web Conference, WWW ’13, pp. 1–12. International World Wide Web Conferences Steering Committee/ACM, Rio de Janeiro, Brazil, 13–17 May 2013Google Scholar
  2. 2.
    Addis, A., Armano, G., Giuliani, A., Vargiu, E.: A recommender system based on a generic contextual advertising approach. In: Proceedings of the 15th IEEE Symposium on Computers and Communications, ISCC 2010, pp. 859–861. IEEE, Riccione, Italy, 22–25 June 2010Google Scholar
  3. 3.
    Addis, A., Armano, G., Vargiu, E.: Assessing progressive filtering to perform hierarchical text categorization in presence of input imbalance. In: Fred, A.L.N., Filipe, J. (eds.) KDIR 2010—Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 14–23. SciTePress, Valencia, Spain, 25–28 Oct 2010Google Scholar
  4. 4.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  5. 5.
    Armano, G., Vargiu, E.: A unifying view of contextual advertising and recommender systems. In: Fred, A.L.N., Filipe, J. (eds.) KDIR 2010—Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 463–466. SciTePress, Valencia, Spain, 25–28 Oct 2010Google Scholar
  6. 6.
    Armano, G., Giuliani, A., Vargiu, E.: Semantic enrichment of contextual advertising by using concepts. In: Filipe, J., Fred, A.L.N. (eds.) KDIR 2011—Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 232–237. SciTePress, Paris, France, 26–29 Oct 2011Google Scholar
  7. 7.
    Armano, G., Giuliani, A., Vargiu, E.: Studying the impact of text summarization on contextual advertising. In: Morvan, F., Tjoa, A.M., Wagner, R. (eds.) 2011 Database and Expert Systems Applications, DEXA, International Workshops, pp. 172–176. IEEE Computer Society, Toulouse, France, 29 Aug–2 Sept 2011Google Scholar
  8. 8.
    Bennett, J., Elkan, C., Liu, B., Smyth, P., Tikk, D.: Kdd cup and workshop 2007. SIGKDD Explor. Newsl. 9(2), 51–52 (2007)CrossRefGoogle Scholar
  9. 9.
    Burke, R.D., 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)Google Scholar
  10. 10.
    Daniel Billsus, M.J.P.: Learning collaborative information filters. In: Shavlik, J.W. (ed.) Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), pp. 46–54. Morgan Kaufmann, Madison, Wisconsin, USA, 24–27 July 1998Google Scholar
  11. 11.
    Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer (2011)Google Scholar
  12. 12.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)Google Scholar
  13. 13.
    Festinger, L.: A Theory of Cognitive Dissonance, vol. 2. Stanford university press (1962)Google Scholar
  14. 14.
    Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G., Filannino, M., Molino, P.: Introducing serendipity in a content-based recommender system. In: HIS, pp. 168–173. IEEE Computer Society (2008)Google Scholar
  15. 15.
    Koren, Y., Bell, R.M.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 145–186. Springer (2011)Google Scholar
  16. 16.
    Koren, Y., Volinsky, C., Bell, R.M.: Matrix factorization techniques for recommender systems. IEEE. Computer 42(8), 30–37 (2009)Google Scholar
  17. 17.
    Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer (2011)Google Scholar
  18. 18.
    Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, AND 2010 (in conjunction with CIKM 2010). ACM, Toronto, Ontario, Canada, 26th Oct 2010Google Scholar
  19. 19.
    Mislove, A., Lehmann, S., Ahn, Y., Onnela, J., Rosenquist, J.N.: Understanding the demographics of twitter users. In: Proceedings of the Fifth International Conference on Weblogs and Social Media. The AAAI Press, Barcelona, Catalonia, Spain, 17–21 July 2011Google Scholar
  20. 20.
    Munson, S.A., Resnick, P.: Presenting diverse political opinions: how and how much. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, pp. 1457–1466. ACM, New York, NY, USA, 2010Google Scholar
  21. 21.
    Murakami, T., Mori, K., Orihara, R.: Metrics for evaluating the serendipity of recommendation lists. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) New Frontiers in Artificial Intelligence, JSAI 2007 Conference and Workshops, Miyazaki, Japan, 18–22 June 2007, Revised Selected Papers, Springer (2008)Google Scholar
  22. 22.
    Pariser, E.: The Filter Bubble: What the Internet Is Hiding from You. The Penguin Group (2011)Google Scholar
  23. 23.
    Park, S., Kang, S., Chung, S., Song, J.: Newscube: delivering multiple aspects of news to mitigate media bias. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems, CHI 2009. ACM, Boston, MA, USA, 4–9 April 2009Google Scholar
  24. 24.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer (2011)Google Scholar
  25. 25.
    Saia, R., Boratto, L., Carta, S.: Semantic coherence-based user profile modeling in the recommender systems context. In: Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014, pp. 154–161. SciTePress, Rome, Italy, 21–24 Oct 2014Google Scholar
  26. 26.
    Saia, R., Boratto, L., Carta, S.: A new perspective on recommender systems: a class path information model. In: Science and Information Conference (SAI), pp. 578–585. IEEE (2015)Google Scholar
  27. 27.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)CrossRefGoogle Scholar
  28. 28.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefMATHGoogle Scholar
  29. 29.
    Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer (2011)Google Scholar
  30. 30.
    Stilo, G., Velardi, P.: Temporal semantics: time-varying hashtag sense clustering. In: Knowledge Engineering and Knowledge Management, volume 8876 of Lecture Notes in Computer Science, pp. 563–578. Springer International Publishing (2014)Google Scholar
  31. 31.
    Stilo, G., Velardi, P.: Time makes sense: event discovery in twitter using temporal similarity. In: Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), WI-IAT ’14, vol. 02, pp. 186–193. IEEE Computer Society, Washington, DC, USA (2014)Google Scholar
  32. 32.
    Stilo, G., Velardi, P.: Efficient temporal mining of micro-blog texts and its application to event discovery. Data Mining and Knowledge Discovery (2015)Google Scholar
  33. 33.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL ’03, vol. 1, pp. 173–180. Association for Computational Linguistics, Stroudsburg, PA, USA (2003)Google Scholar
  34. 34.
    Vargiu, E., Giuliani, A., Armano, G.: Improving contextual advertising by adopting collaborative filtering. ACM Trans. Web 7(3), 13:1–13:22 (2013)Google Scholar
  35. 35.
    Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: RecSys, pp. 123–130. ACM (2008)Google Scholar
  36. 36.
    Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Ellis, A., Hagino, T. (eds.) Proceedings of the 14th international conference on World Wide Web, WWW 2005, pp. 22–32. ACM, Chiba, Japan, 10–14 May 2005Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Roberto Saia
    • 1
  • Ludovico Boratto
    • 1
  • Salvatore Carta
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità di CagliariCagliariItaly

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