Abstract
A user’s informational need and preferences can be modeled by criteria, which in turn can be used to prioritize candidate results and produce a ranked list. We examine the use of such a criteria-based user model separately in two representative recommendation tasks: news article recommendations and product recommendations. We ask the following: are there nonlinear interactions among the criteria; and should the models be personalized? We assume that that user ratings on each criterion are available, and use machine learning to infer a user model that combines these multiple ratings into a single overall rating. We found that the ratings of different criteria have a nonlinear interaction in some cases, for example, article novelty and subject relevance often interact. We also found that these interactions vary from user to user.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Triantaphyllou, E.: Multi-Criteria Decision Making Methods: A Comparative Study. Kluwer Academic Publishers, Dordrecht (2000)
Wolfe, S.R., Zhang, Y.: User-centric multi-criteria information retrieval. In: Allan, J., Aslam, J., Sanderson, M., Zhai, C., Zobel, J. (eds.) SIGIR ’09: Proceedings of the 32nd international ACM conference on Research and development in information retrieval, pp. 818–819. ACM, New York (2009)
Chen, H., Karger, D.R.: Less is more: probabilistic models for retrieving fewer relevant documents. In: Efthimiadis, E.N., Dumais, S., Hawking, D., Järvelin, K. (eds.) SIGIR ’06: Proceedings of the 29th annual international ACM conference on Research and development in information retrieval, pp. 429–436. ACM Press, New York (2006)
Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: Croft, W.B., Moffat, A., van Rijsbergen, C.J., Wilkinson, R., Zobel, J. (eds.) SIGIR ’98: Proceedings of the 21st annual international ACM conference on Research and development in information retrieval, pp. 335–336. ACM, New York (1998)
Zhang, Y., Callan, J., Minka, T.: Novelty and redundancy detection in adaptive filtering. In: Järvelin, K., Beaulieu, M., Baeza-Yates, R., Myaeng, S.H. (eds.) SIGIR ’02: Proceedings of the 25th annual international ACM conference on Research and development in information retrieval, pp. 81–88. ACM, New York (2002)
Allan, J., Wade, C., Bolivar, A.: Retrieval and novelty detection at the sentence level. In: Clarke, C., Cormack, G., Callan, J., Hawking, D., Smeaton, A. (eds.) SIGIR ’03: Proceedings of the 26th annual international ACM conference on Research and development in information retrieval, pp. 314–321. ACM, New York (2003)
Barry, C.L.: User-defined relevance criteria: an exploratory study. J. Am. Soc. Inf. Sci. 45(3), 149–159 (1994)
Maglaughlin, K., Sonnenwald, D.: User perspectives on relevance criteria: a comparison among relevant, partially relevant, and not-relevant judgements. J. Am. Soc. Inf. Sci. Technol. 53(5), 327–342 (2002)
Tombros, A., Ruthven, I., Jose, J.M.: How users assess web pages for information seeking. J. Am. Soc. Inf. Sci. Technol. 56(4), 327–344 (2005)
Manouselis, N., Costopoulou, C.: Analysis and classification of multi-criteria recommender systems. World Wide Web 10(4), 415–441 (2007)
Pasi, G., Bordogna, G., Villa, R.: A multi-criteria content-based filtering system. In: Kraaij, W., de Vries, A.P., Clarke, C.L.A., Fuhr, N., Kando, N. (eds.) SIGIR ’07: Proceedings of the 30th Annual International ACM conference on Research and Development in Information Retrieval, pp. 775–776. ACM, New York (2007)
de Gemmis, M., Semeraro, G., Lops, P., Basile, P.: A retrieval model for personalized searching relying on content-based user profiles. In: Mobasher, B., Anand, S.S., Kobsa, A., Jannach, D. (eds.) 6th AAAI Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2008), pp. 1–9 (2008)
Pognačnik, M., Tasič, J., Košir, A.: Optimization of multi-attribute user modeling approach. International Journal of Electronics and Communications 58(4), 402–412 (2004)
Nguyen, H., Haddawy, P.: The decision-theoretic video advisor. In: Kautz, H. (ed.) AAAI-98 Workshop on Recommender Systems, pp. 77–80 (1998)
Chajewska, U., Koller, D.: Utilities as random variables: Density estimation and structure discovery. In: Boutilier, C., Goldszmidt, M. (eds.) Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 63–71 (2000)
Robu, V., Somefun, D.J.A., La Poutré, J.A.: Modeling complex multi-issue negotiations using utility graphs. In: Pechoucek, M., Steiner, D., Thompson, S. (eds.) AAMAS ’05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, pp. 280–287. ACM, New York (2005)
Zhang, Y.: Yow user study data: Implicit and explicit feedback for news recommendation, http://www.soe.ucsc.edu/~yiz/papers/data/YOWStudy
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wolfe, S.R., Zhang, Y. (2010). Interaction and Personalization of Criteria in Recommender Systems. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-13470-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13469-2
Online ISBN: 978-3-642-13470-8
eBook Packages: Computer ScienceComputer Science (R0)