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Mediation of user models for enhanced personalization in recommender systems

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Abstract

Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The work discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and cross-representation. Finally, the work reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users.

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References

  1. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Syst. 23(1), 103–145 (2005)

    Article  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Eng. 17(6), 734–749 (2005a)

    Article  Google Scholar 

  3. Adomavicius, G., Tuzhilin, A.: Personalization technologies: a process-oriented perspective. Commun. ACM 48(10), 83–90 (2005b)

    Article  Google Scholar 

  4. Aguzzoli, S., Avesani, P., Massa, P.: Collaborative case-based recommender system. In: European Conference on Case-Based Reasoning, pp. 460–474. Aberdeen, UK (2002)

  5. Aroyo, L., Schut, H., Nack, F., Schiphorst, T., Kauw-A-Tjoe, M.: Personalized ambient media experience: move.me case study. In: International Conference on Intelligent User Interfaces, pp. 298–301. Honolulu, HI (2007)

  6. Berkovsky, S.: Decentralized mediation of user models for a better personalization. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 404–408. Dublin, Ireland (2006)

  7. Berkovsky, S., Kuflik, T., Ricci, F.: Cross-technique mediation of user models. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 21–30. Dublin, Ireland (2006a)

  8. Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Recommender Systems Conference Minneapolis, MN, (2007c)

  9. Berkovsky, S., Aroyo, L., Heckmann, D., Houben, G.J., Kröner, A., Kuflik, T., Ricci, F.: Providing context-aware personalization through cross-context reasoning of user modeling data. In: Workshop on Decentralized and Ubiquitous User Modeling, pp. 2–7. Corfu, Greece (2007a)

  10. Berkovsky, S., Gorfinkel, A., Kuflik, T., Manevitz, L.: Case-based to content-based user model mediation. In: Workshop on Ubiquitous User Modeling, pp. 1–4. Riva del Garda, Italy (2006b)

  11. Berkovsky, S., Kuflik, T., Ricci, F.: Distributed collaborative filtering with domain specialization. In: Recommender Systems Conference, Minneapolis, MN (2007b)

  12. Brown, P.J., Bovey, J.D., Chen, X.: Context-aware applications: from the laboratory to the marketplace. IEEE Pers. Commun. 4(5), 58–64 (1997)

    Article  Google Scholar 

  13. Buriano, L., Marchetti, M., Carmagnola, F., Cena, F., Gena, C., Torre, I.: The role of ontologies in context-aware recommender systems. In: International Conference on Mobile Data Management, p. 80. Nara, Japan (2006)

  14. Burke, R.: Knowledge-based recommender systems. In: Kent A. (ed.) Encyclopedia of Library and Information Systems, pp. 180–200. Dekker Encyclopedias (2000)

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

    Article  MATH  Google Scholar 

  16. Chen, A.: Context-aware collaborative filtering system: predicting the user’s preference in the ubiquitous computing environment. In: International Workshop on Location- and Context-Awareness, pp. 244–253. Oberpfaffenhofen, Germany (2005)

  17. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Workshop on Recommender Systems, Berkeley, CA, http://www.cs.umbc.edu/~ian/sigir99-rec/ (1999)

  18. Cranor, L.F., Reagle, J., Ackerman, M.S.: Beyond concern: understanding net users’ attitudes about online privacy. Technical Report, AT&T Labs-Research (1999)

  19. Dey, A.K., Abowd, G.D.: Towards a better understanding of context and context-awareness. In: International Symposium on Handheld and Ubiquitous Computing, pp. 304–307. Karlsruhe, Germany (1999)

  20. Finin, T.W., Drager, D.: A general user modeling system. In: Canadian Conference on Artificial Intelligence, pp. 24–29. Montreal, Canada (1986)

  21. Finin, T.W.: GUMS: a general user modeling shell. In: Kobsa, A., Wahlster, W. (eds.) User Models in Dialog Systems, pp. 411–430. Springer Publishers (1989)

  22. Francisco-Revilla, L., Shipman, M.: Managing conflict in multi-model adaptive hypertext. In: Conference on Hypertext and Hypermedia, pp. 237–238. Santa Cruz, CA (2004)

  23. Goker, A., Myrhaug, H.I.: User context and personalization. In: Workshop on Case Based Reasoning and Personalization, pp. 4–7. Aberdeen, UK (2002)

  24. Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inform. Retr. 4(2), 133–151 (2001)

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  26. 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: National Conference on Artificial Intelligence, pp. 439–446. Orlando, FL (1999)

  27. Hanani, U., Shapira, B., Shoval, P.: Information filtering: overview of issues, research and systems. User Model. User Adapt. Interact. 11(3), 203–259 (2001)

    Article  MATH  Google Scholar 

  28. Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., Wilamowitz-Moellendorff, M.V.: GUMO—the general user model ontology. In: International Conference on User Modeling, pp. 428–432. Edinburgh, UK (2005)

  29. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: International Conference on Research and Development in Information Retrieval, pp. 230–237. Berkeley, CA (1999)

  30. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Syst. 22(1), 5–53 (2004) Internet Movie Database, http://www.imdb.com

    Google Scholar 

  31. Joachims, T., Freitag, D., Mitchell, T.: WebWatcher: a tour guide for the World Wide Web. In: International Joint Conference on Artificial Intelligence, pp. 770–777. Nagoya, Japan (1997)

  32. Keidl, M., Kemper, A.: Towards context-aware adaptable web services. In: International World-Wide Web Conference, pp. 55–65. New York, NY (2004)

  33. Kay, J.: Ontologies for reusable and scrutable student model. In: Workshop on Workshop on Ontologies for Intelligent Educational Systems, pp. 72–77. Le Mans, France (1999)

  34. Kay, J., Kummerfeld, B., Lauder, P.: Managing private user models and shared personas. In: Workshop on User Modeling in Ubiquitous Computing, pp. 1–11. Pittsburgh, PA (2003)

  35. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Machine Intell. 20(3), 226–239 (1998)

    Article  Google Scholar 

  36. Kobsa, A.: Generic User Modeling Systems. User Model. User-Adapt. Interact. 11(1–2), 49–63 (2001)

    Article  MATH  Google Scholar 

  37. Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationships. Knowledge Eng. Rev. 16(2), 111–155 (2001)

    Article  MATH  Google Scholar 

  38. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. J. 97(1–2), 273–324 (1997)

    Article  MATH  Google Scholar 

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

    Google Scholar 

  40. Kuflik, T., Sheidin, J., Jbara, S., Goren-Bar, D., Soffer, P., Stock, O., Zancanaro M.: Supporting small groups in the museum by context-aware communication services. In: International Conference on Intelligent User Interfaces, pp. 305–308. Honolulu, HI (2007)

  41. Lemire, D., Boley, H., McGrath, S., Ball, M.: Collaborative filtering and inference rules for context-aware learning object recommendation. J. Interact. Technol. Smart Educ. 2(3), 179–188 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  43. Maes, P.: Agents that reduce work and information overload. Commun. ACM 37(7), 31–40 (1994)

    Article  Google Scholar 

  44. Manouselis, N., Sampson, D.: A multi-criteria model to support automatic recommendation of e-learning quality approaches. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 518–526. Chesapeake, VA (2004)

  45. McJones, P.: EachMovie Collaborative Filtering Data Set. HP Research (1997)

  46. McNee, S.M., Lam, S., Konstan, J., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: International Conference on User Modeling, pp. 178–183. Pittsburgh, PA (2003)

  47. Mehta, B., Niederée, C., Stewart, A., Degemmis, M., Lops, P., Semeraro, G.: Ontologically enriched user profiling for cross system personalization. In: International Conference on User Modeling, pp. 119–123. Edinburgh, UK (2005)

  48. Montaner, M., Lopez, B., Rosa, J.L.: A taxonomy of recommender agents on the internet. AI Rev. 19(4), 285–330 (2003)

    Google Scholar 

  49. Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match retrieval. In: International Conference on Research and Development in Information Retrieval, pp. 272–281. Dublin, Ireland (1994)

  50. Mulvenna, M.D., Anand, S.S., Buchner, A.G.: Personalization on the net using web mining. Commun. ACM 43(8), 123–125 (2000)

    Article  Google Scholar 

  51. Oku, K., Nakajima, S., Miyazaki, J., Uemura, S.: Investigation for designing of context-aware recommendation system using SVM. In: International MultiConference of Engineers and Computer Scientists, pp. 970–975. Hong Kong, Hong Kong (2007)

  52. Pascoe, J.: Adding generic contextual capabilities to wearable computers. In: International Symposium on Wearable Computers, pp. 92–99. Pittsburgh, PA (1998)

  53. Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)

    Article  Google Scholar 

  54. Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)

    Article  Google Scholar 

  55. Potonniee, O.: A decentralized privacy-enabling TV personalization framework. In: European Conference on Interactive Television, pp. 57–66. Brighton, UK (2002)

  56. Rabanser, U., Ricci, F.: Recommender systems: do they have a viable business model in e-tourism? In: Conference of the International Federation for IT & Travel and Tourism, pp. 160–171. Lausanne, Switzerland (2005)

  57. Razmerita, L., Angehrn, A., Maedche, A.: Ontology-based user modeling for knowledge management systems. In: International Conference on User Modeling, pp. 213–217. Pittsburgh, PA (2003)

  58. Resource Description Framework, http://www.w3c.org/RDF/

  59. Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of Netnews. In: International Conference on Computer-Supported Collaborative Work, pp. 175–186. Chapel Hill, NC (1994)

  60. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  61. Ricci, F., Arslan, B., Mirzadeh, N., Venturini, A.: ITR: a case-based travel advisory system. In: European Conference on Case-Based Reasoning, pp. 613–627. Aberdeen, UK (2002)

  62. Ricci, F.: Recommendations in context. In: International Conference on Electronic Commerce and Web Technologies, Krakow, Poland, keynote talk (2006)

  63. Ricci, F., Cavada, D., Mirzadeh, N., Venturini, A.: Case-based travel recommendations. In: Fesemaier, D.R., Werthner, H., Wöber, K.W. (eds.) Destination Recommendation Systems: Behavioural Foundations and Applications, pp. 67–93. CABI publishers (2006)

  64. Sakagami, H., Kamba, T.: Learning personal preferences on online newspaper articles for user behaviors. Comput. Networks ISDN Syst. 29(8–13), 1447–1455 (1997)

    Article  Google Scholar 

  65. Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill publishers (1983)

  66. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering algorithms. In: International World-Wide Web Conference, pp. 285–295. Hong Kong, Hong Kong (2001)

  67. Schafer, J.B., Konstan, J., Riedl, J.: Electronic commerce recommender applications. J. Data Mining Knowledge Discov. 5(1–2), 115–152 (2000)

    Google Scholar 

  68. Setten, M.V., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application COMPASS. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 235–244. Eindhoven, the Netherlands (2004)

  69. Shardanand, U., Maes, P.: Social information filtering: algorithms for automating ‘word of mouth’. In: International Conference on Human Factors in Computing Systems, pp. 210–217. Pittsburgh, PA, (1995)

  70. Vozalis, M., Margaritis, K.G.: Enhancing collaborative filtering with demographic data: the case of item-based filtering. In: International Conference on Intelligent Systems Design and Applications, pp. 361–366. Budapest, Hungary (2004)

  71. Wahlster, W., Kröner, A., Heckmann, D.: SharedLife: towards selective sharing of augmented personal memories. In: Sotck, O., Schaerf, M. (eds.) Reasoning, Action and Interaction in AI Theories and Systems, pp. 327–342. Springer Publishers (2006)

  72. Wang, Y., Kobsa, A.: Impacts of privacy laws and regulations on personalized systems. In: Workshop on Privacy-Enhanced Personalization, pp. 44–46. Montreal, Canada (2006)

  73. Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: International World-Wide Web Conference, pp. 22–32. Chiba, Japan (2005)

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Correspondence to Shlomo Berkovsky.

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Berkovsky, S., Kuflik, T. & Ricci, F. Mediation of user models for enhanced personalization in recommender systems. User Model User-Adap Inter 18, 245–286 (2008). https://doi.org/10.1007/s11257-007-9042-9

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