Abstract
A recommender system assists customers in product selection by matching client preferences to suitable items. This paper describes a preference matching technique for products categorized by a faceted feature classification scheme. Individual ratings of features and products are used to identify a customer’s predictive neighborhood. A recommendation is obtained by an inferred ranking of candidate products drawn from the neighborhood. The technique addresses the problem of sparse customer activity databases characteristic of e-commerce. Product search is conducted in a controlled, effective manner based on customer similarity. The inference mechanism evaluates the probabilty that a candidate product satisfies a customer query. The inference algorithm is presented and illustrated by a practical example.
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References
Aggarwal, C., Wolf, J., Wu, K. and Yu, P. Horting Hatches an Egg: a New Graph-theoretic Approach to Collaborative Filtering. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA (1999)201–212
Breese, J., Heckerman, D. and Kadie, C. Empirical analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (1998) 43–52
Damiani, E., Fugini, E. and Bellettini, C. A Hierarchy-Aware Approach to Faceted Classification of Object-Oriented Components. ACM Transactions on Software Engineering and Methodology 8:3 (1999) 215–262
Goldberg, D., Nichols, D., Oki, B. and Terry, D. Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35:12 (1992) 61–70
Heckerman, D. A Tutorial on Learning with Bayesian Networks. Microsoft Technical Report MSR-TR 95-06 (1995)
Heckerman, D. and Wellman, M. Bayesian Networks. Communications of the ACM 38:3 (1995) 27–30
Herlocker, J., Konstan, J., Borchers, A. and 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, Berkeley, CA (1999) 230–237
Hill, W., Stead, L., Rosenstein, M. and Furnas, G. Recommending and evaluating choices in a virtual community of use. In Proceedings of ACM Conference on Human Factors in Computing Systems, Denver, CO (1995) pages 194–201.
Howard R. and Matheson, J. (eds.). The Principles and Applications of Decision Analysis. Strategic Decisions Group, Menlo Park, CA (1983)
Loney, F. Normalization of a Bounded Observation Set (or How to Compare Apples and Oranges). Spirited Software Technical Report SSI-TR 2001-01, available at http://www.spiritedsw.com/pub/techreports/tr2001-01.pdf (2001)
Prieto-Díaz, R. Implementing faceted classification for software reuse. Communications of the ACM 34:5 (1991) 88–97
Resnick, P., Iacovou, N. Suchak, M., Bergstrom, P. and Riedl, J. GroupLens: an Open Architecture for Collaborative Filtering of Netnews. In Proceedings of the Fifth ACM Conference on Computer Supported Cooperative Work, Chapel Hill, NC (1994) 175–186
Ribeiro, B. and Muntz, R. A Belief Network Model for IR. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Zurich, Switzerland (1996) 253–260
Sarwar, B., Karypis, G., Knowtan, J. and Riedl, J. Analysis of Recommendation Algorithms for E-Commerce. In Proceedings of the Second ACM Conference on Electronic Commerce, Minneapolis, MI (2000) 158–167
Schafer, B., Konstan, Riedl, J. Recommender Systems in E-Commerce. In Proceedings of the First ACM Conference on Electronic Commerce, Denver, CO (1999) 158–166
Schapire, R. and Singer, Y. Improved boosting algorithms using using confidence-rated predictions. Machine Learning 37:3 (1999) 297–336
Shardanand, U. and Patti Maes, P. 1995. Social information filtering: Algorithms for automating “word of mouth”. In Proceedings of ACM Conference on Human Factors in Computing Systems, Denver, CO (1995) 210–217
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Loney, F.N. (2001). Faceted Preference Matching in Recommender Systems. In: Bauknecht, K., Madria, S.K., Pernul, G. (eds) Electronic Commerce and Web Technologies. EC-Web 2001. Lecture Notes in Computer Science, vol 2115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44700-8_28
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DOI: https://doi.org/10.1007/3-540-44700-8_28
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