Abstract
Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users, according to their characteristics and preferences, as represented by a User Model (UM). Since the quality of the personalization largely depends on the size and accuracy of the managed UMs, it would be beneficial to enrich the UMs by mediating, i.e., importing and integrating, UMs built by other personalization systems. This work discusses and evaluates a cross-representation mediation of UMs from collaborative filtering to content-based recommender systems. According to this approach, a content-based recommender system, having partial or no UM data, can generate recommendations for users by mediating UM data of the same users, collected by a collaborative filtering system. The mediation process transforms the UMs from the collaborative filtering ratings to the content-based weighted item features. The mediation process exploits the item descriptions that are typically not used by the collaborative filtering recommender systems. An experimental evaluation conducted in the domain of movies shows that for users with small collaborative filtering UMs, i.e., users with few item ratings, the accuracy of the recommendations provided using the mediated content-based UMs is superior to that using the original collaborative filtering UMs. Moreover, it shows that the mediation can be used to improve a content-based recommender system by incrementally mediating collaborative filtering UM data (item ratings) and enriching the available content-based UMs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Balabanovic M., Shoham Y.: Content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26–30, pp. 714–720 (1998)
Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Proceedings of the Recommender Systems Conference, RecSys 2007, October 19–20, pp. 9–16 (2007)
Berkovsky S., Kuflik T., Ricci F.: Mediation of user models for enhanced personalization in recommender systems. User Model. User-Adapt. Interact. 18(3), 245–286 (2008)
Bernstein, P.A., Melnik, S.: Meta data management. In: Proceedings of the 20th International Conference on Data Engineering, ICDE 2004, 30 March–2 April, 875 pp (2004)
Billsus D., Pazzani M.J.: User modeling for adaptive news access. User Model. User-Adapt. Interact. 10(2–3), 147–180 (2000)
Burke R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)
Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: GUMO—the general user model ontology. In: Proceedings of User Modeling 2005, 10th International Conference, UM 2005, Edinburgh, Scotland, UK, July 24–29, pp. 428–432 (2005)
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 ’99), August 15–19, pp. 230–237 (1999)
Herlocker J.L., Konstan J.A., Terveen L.G., Riedl J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Kay, J., Kummerfeld, B., Lauder, P.: Managing private user models and shared personas. In: Proceedings of the UbiUM Workshop (2003)
Kobsa A.: Generic user modeling systems. User Model. User-Adapt. Interact. 11(1–2), 49–63 (2001)
Kohavi R., John G.H.: Wrappers for feature subset selection. Artif. Intell. J. 97(1–2), 273–324 (1997)
Lorenz, A.: A specification for agent-based distributed user modeling in ubiquitous computing. In: Proceedings of the DASUM Workshop (2005)
McJones, P.: EachMovie Collaborative Filtering Data Set. HP Research (1997)
McNee, S.M., Lam, S., Konstan, J., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: Proceedings of User Modeling 2003, 9th International Conference, UM 2003, Johnstown, PA, USA, June 22–26, pp. 178–187 (2003)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Proceedings of the 2006 Conference on Human Factors in Computing Systems, CHI 2006, Montreal, Quebec, Canada, April 22–27, pp. 1097–1101 (2006)
Mehta, B., Nejdl, W.: Unsupervised strategies for shilling detection and robust collaborative filtering. User Model. User Adapt. Interact. (2008) doi:10.1007/s11257-008-9050-4.
Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match retrieval. In: Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, 3–6 July, pp. 272–281 (1994)
Mulvenna M.D., Anand S.S., Buchner A.G.: Personalization on the net using web mining. Commun. ACM 43(8), 123–125 (2000)
Niederee, C., Stewart, A., Mehta, B., Hemmje, M.: A multi-dimensional, unified user model for cross-system personalization. In: Proceedings of the E4PIA Workshop (2004)
Pazzani M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)
Resnick P., Varian H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall publishers (1995)
Stamou, S., Ntoulas, A.: Search personalization through query and page topical analysis. User Model. User Adapt. Interact. (2008) doi:10.1007/s11257-008-9056-y.
Tintarev, N.: Explaining recommendations. In: Proceedings of User Modeling 2007, 11th International Conference, UM 2007, Corfu, Greece, June 25–29, pp. 470–474 (2007)
Zanker, M., Jessenitschnig, M.: Case-studies on exploiting explicit customer requirements in recommender systems. User Model. User Adapt. Interact. (2008) doi:10.1007/s11257-008-9048-y.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Berkovsky, S., Kuflik, T. & Ricci, F. Cross-representation mediation of user models. User Model User-Adap Inter 19, 35–63 (2009). https://doi.org/10.1007/s11257-008-9055-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11257-008-9055-z