Learning Preference Models in Recommender Systems

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Marco de Gemmis , Leo Iaquinta, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Giovanni Semeraro: Learning Preference Models in Recommender Systems. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 387-407.




As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular applications of principles and techniques coming from Information Filtering (IF). As IF techniques usually perform a progressive removal of nonrelevant content according to the information stored in a user profile, recommendation algorithms process information about user interests – acquired in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way – and exploit these data to generate a list of recommended items. Although each type of filtering method has its own weaknesses and strengths, preference handling is one of the core issues in the design of every recommender system: since these systems aim to guide users in a personalized way to interesting or useful objects in a large space of possible options, it is important for them to accurately capture and model user preferences. The goal of this chapter is to provide a general overview of the approaches to learning preference models in the context of recommender systems. In the first part, we introduce general concepts and terminology of recommender systems, giving a brief analysis of advantages and drawbacks for each filtering approach. Then we will deal with the issue of learning preference models, show the most popular techniques for profile learning and preference elicitation, and analyze methods for feedback gathering in recommender systems.

Extended Abstract


booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
title={Learning Preference Models in Recommender Systems},
url={http://dx.doi.org/10.1007/978-3-642-14125-6_18, http://de.evo-art.org/index.php?title=Learning_Preference_Models_in_Recommender_Systems },
publisher={Springer Berlin Heidelberg},
author={Gemmis, Marcode and Iaquinta, Leo and Lops, Pasquale and Musto, Cataldo and Narducci, Fedelucio and Semeraro, Giovanni},

Used References

1. J. Ahn, P. Brusilovsky, J. Grady, D. He, S. Yeon Syn, Open user profiles for adaptive news systems: help or harm? in Proceedings of the 16th International Conference on World Wide Web, ed. by Carey L. Williamson, Mary Ellen Zurko, Peter F. Patel-Schneider, Prashant J. Shenoy (ACM, 2007), pp. 11–20

2. R. Alton-Scheidl, R. Schumutzer, P.P. Sint, G. Tscherteu. Voting and rating in Web4Groups (R. Oldenbourg, 1997), pp. 13–103

3. R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval (Addison-Wesley, 1999)

4. M. Balabanovic, Y. Shoham, Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997) http://dx.doi.org/10.1145/245108.245124

5. N. Belkin, B. Croft, Information filtering and information retrieval:two sides of the same coin? Commun. ACM 35(12), 29–37 (1992) http://dx.doi.org/10.1145/138859.138861

6. D. Billsus, M. Pazzani, Learning probabilistic user models, in Proceedings of the Workshop on Machine Learning for User Modeling (Chia Laguna, IT, 1997)

7. D. Billsus, M.J. Pazzani, A hybrid user model for news story classification, in Proceedings of the Seventh International Conference on User Modeling (Banff, Canada, 1999)

8. D. Billsus, M.J. Pazzani, User modeling for adaptive news access. User Model. User-Adapt. Interact. 10(2-3), 147–180 (2000) http://dx.doi.org/10.1023/A%3A1026501525781

9. R. Brafman, C. Domshlak, Preference handling - an introductory tutorial. AI Mag. 30(1), 58–86 (2009)

10. R. Burke, Knowledge-based recommender systems, in Encyclopedia of Library and Information Systems, vol. 69, no. 32, ed. by A. Kent (2000)

11. R. Burke, Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002) http://dx.doi.org/10.1023/A%3A1021240730564

12. P. Clark, T. Niblett, The cn2 induction algorithm. Mach. Learn. 3, 261–283 (1989)

13. M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, M. Sartin, Combining content-based and collaborative filters in an online newspaper, in Proceedings of ACM SIGIR Workshop on Recommender Systems (1999)

14. W.W. Cohen, Fast effective rule induction, in Proceedings of the 12th International Conference on Machine Learning (ICML’95) (1995), pp. 115–123

15. W.W. Cohen, Y. Singer, A simple, fast, and effictive rule learner, iIn AAAI/IAAI (1999), pp. 335–342

16. M. Degemmis, P. Lops, G. Semeraro, A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. User Model. User-Adapt. Interact. J. Per. Res. (UMUAI), 17(3), 217–255 (Springer Science + Business Media B.V., 2007)

17. P. Domingos, M.J. Pazzani, On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Mach. Learn. 29(2-3), 103–130 (1997) http://dx.doi.org/10.1023/A%3A1007413511361

18. J. Grudin, Groupware and social dynamics: eight challenges for developers. Commun. ACM 37(1), 92–105 (1994) http://dx.doi.org/10.1145/175222.175230

19. J.L. Herlocker, J.A. Konstan, A. Borchers, J. Riedl, An algorithmic framework for performing collaborative filtering, in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Theoretical Models (1999), pp. 230–237

20. A. Jennings, H. Higuchi, A user model neural network for a personal news service. User Model. User-Adapt. Interact. 3(1), 1–25 (1993) http://dx.doi.org/10.1007/BF01099423

21. T. Joachims, D. Freitag, T.M. Mitchell, Web Watcher: A Tour Guide for the World Wide Web, in 15th International Joint Conference on Artificial Intelligence (1997), pp. 770–777

22. K. Keenoy, M. Levene, Personalisation of web search. in Intelligent Techniques for Web Personalization, vol. 3169 Lecture Notes in Computer Science, ed. by B. Mobasher, S.S. Anand (Springer, 2003), pp. 201–228

23. D. Kelly, J. Teevan, Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2), 18–28 (2003) http://dx.doi.org/10.1145/959258.959260

24. J. Kim, D.W. Oard, K. Romanik, Using implicit feedback for user modeling in internet and intranet searching. Technical report, University of Maryland at College Park, 2000

25. B. Krulwich, Lifestyle finder: Intelligent user profiling using large-scale demographic data. Artif. Intell. Mag. 18(2), 37–45 (1997)

26. K. Lang, Newsweeder: Learning to filter news, in Proceedings of the 12th International Conference on Machine Learning (Lake Tahoe, CA, 1995), pp. 331–339

27. W.S. Lee, Collaborative learning for recommender systems, in Procedings of the International Conference on Machine Learning (2001)

28. D.D. Lewis, M. Ringuette, A comparison of two learning algorithms for text categorization, in Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval (Las Vegas, US, 1994), pp. 81–93

29. G. Linden, B. Smith, J. York, Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

30. F. Lorenzi, F. Ricci, Case-based recommender systems: a unifying view, in ITWP, vol. 3169, Lecture Notes in Computer Science, ed. by B. Mobasher, S.S. Anand (Springer, 2003), pp. 89–113

31. P. Melville, R.J. Mooney, R. Nagarajan, Content-boosted collaborative filtering for improved recommendations, in Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-02) (AAAI, Menlo Parc, CA, USA, 2002), pp. 187–192

32. S.E. Middleton, N.R. Shadbolt, D.C. De Roure, Ontological User Profiling in Recommender Systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004) http://dx.doi.org/10.1145/963770.963773

33. B.N. Miller, I. Albert, S.K. Lam, J.A. Konstan, J. Riedl, Movielens unplugged: experiences with an occasionally connected recommender system, In IUI ’03: Proceedings of the 8th international conference on Intelligent user interfaces (ACM, New York, NY, USA, 2003), (pp. 263–266)

34. M. Montaner, B. Lopez, J.L. De La Rosa, A Taxonomy of Recommender Agents on the Internet. Artif. Intell. Rev. 19(4), 285–330 (2003) http://dx.doi.org/10.1023/A%3A1022850703159

35. R.J. Mooney, L. Roy, Content-Based Book Recommending Using Learning for Text Categorization, in Proceedings of the 5th ACM Conference on Digital Libraries (ACM, San Antonio, US, New York, US, 2000), pp. 195–204

36. M. Morita, Y. Shinoda, Information filtering based on user behavior analysis and best match text retrieval, in SIGIR ’94: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval (New York, NY, USA, 1994), pp. 272–281

37. D.M. Nichols, Implicit rating and filtering, in Proceedings of Fifth DELOS Workshop on Filtering and Collaborative Filtering (ERCIM, 1997), pp. 31–36

38. M. Pazzani, D. Billsus, Learning and Revising User Profiles: The Identification of Interesting Web Sites. Mach. Learn. 27(3), 313–331 (1997) http://dx.doi.org/10.1023/A%3A1007369909943

39. M.J. Pazzani, A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5-6), 393–408 (1999) http://dx.doi.org/10.1023/A%3A1006544522159

40. M.J. Pazzani, J. Muramatsu, D. Billsus, Syskill and Webert: Identifying interesting web sites, in Proceedings of the Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference (AAAI/MIT, Menlo Park, 1996), pp. 54–61

41. P. Pu, L. Chen, User-involved preference elicitation for product search and recommender systems. AI Mag. 29(4), 93–103 (2008)

42. P. Pu, B. Faltings, Enriching buyers’ experiences: the smartclient approach, in CHI ’00: Proceedings of the SIGCHI conference on Human factors in computing systems (ACM, New York, NY, USA, 2000), pp. 289–296

43. J.R. Quinlan, Learning efficient classification procedures and their application to chess end games, in Machine Learning. An Artificial Intelligence Approach (1983), pp. 463–482

44. J.R. Quinlan, The minimum description length principle and categorical theories, in ICML (1994), pp. 233–241

45. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl, GroupLens: An Open Architecture for Collaborative Filtering of Netnews, in Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work (ACM, Chapel Hill, North Carolina, 1994), pp. 175–186

46. F. Ricci, Q.N. Nguyen, Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intell. Syst. 22(3), 22–29 (2007) http://dx.doi.org/10.1109/MIS.2007.43

47. E. Rich, User Modeling via Stereotypes. Cogn. Sci. 3, 329–354 (1979) http://dx.doi.org/10.1207/s15516709cog0304_3

48. J.J. Rocchio, Relevance Feedback in Information Retrieval (Prentice Hall, Englewood, Cliffs, New Jersey, 1971)

49. J. Salter, N. Antonoupoulos, CinemaScreen Recommender Agent: Combining collaborative and content-based filtering, IEEE Intell. Syst. 21(1), 35–41 (2006) http://dx.doi.org/10.1109/MIS.2006.4

50. G. Salton, A. Wong, C.S. Yang, A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975) http://dx.doi.org/10.1145/361219.361220

51. G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval. Technical report, 1988

52. S.N. Schiaffino, A. Amandi, User profiling with case-based reasoning and bayesian networks, in IBERAMIA-SBIA 2000 Open Discussion Track (2000), pp. 12–21

53. F. Sebastiani, Machine Learning in Automated Text Categorization. ACM Comput. Surv. 34(1), 1–47 (2002) http://dx.doi.org/10.1145/505282.505283

54. G. Semeraro, P. Basile, M. de Gemmis, P. Lops, User Profiles for Personalizing Digital Libraries, in Handbook of Research on Digital Libraries: Design, Development and Impact, ed. by Yin-Leng Theng, Schubert Foo, Dion Goh Hoe Lian, Jin-Cheon Na (IGI Global, 2009), pp. 149–158. ISBN 978-159904879-6

55. B. Sheth, P. Maes, Evolving agents for personalized information filtering, in Proceedings of the Ninth Conference on Artificial Intelligence for Applications (IEEE Computer Society, 1993), pp. 345–352

56. B. Smith, P. Cotter, A Personalized TV Listings Service for the Digital TV Age. Knowl. Based Syst. 13, 53–59 (2000) http://dx.doi.org/10.1016/S0950-7051(00)00046-0

57. F.C. Stevens, Knowledge-based assistance for accessing large, poorly structured information spaces. PhD thesis, Boulder, CO, USA, 1993

58. L. Terveen, W. Hill, Human-Computer Collaboration in Recommender Systems, in HCI on the new Millennium, ed. by J. Carroll (Addison Wesley, 2001)

59. B. Towle, C. Quinn, Knowledge based recommender systems using explicit user models, in Papers from the AAAI Workshop, AAAI Technical Report WS-00-04 (AAAI, Menlo Park, CA, 2000), pp. 74–77

60. A.M. Wasfi, Collecting user access patterns for building user profiles and collaborative filtering, in Proceedings of the International Conference on Intelligent User Interfaces (1999), pp. 57–64

61. R. White, I. Ruthven, J.M. Jose, The use of implicit evidence for relevance feedback in web retrieval, in Proceedings of the 24th BCS-IRSG European Colloquium on IR Research (Springer, London, UK, 2002), pp. 93–109

62. Y. Yang, J.O. Pedersen, A Comparative Study on Feature Selection in Text Categorization, in Proceedings of ICML-97, 14th International Conference on Machine Learning, ed. by Douglas H. Fisher (Morgan Kaufmann, Nashville, San Francisco, US, 1997), pp. 412–420


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