Abstract
Initial successes in the area of recommender systems have led to considerable early optimism. However as a research community, we are still in the early days of our understanding of these applications and their capabilities. Evaluation metrics continue to be refined but we still need to account for the relative contributions of the various knowledge elements that play a part in the recommendation process. In this paper, we make a fine-grained analysis of a successful case-based recommendation approach, providing an ablation study of similarity knowledge and similarity metric contributions to improved system performance. In particular, we extend our earlier analyses to examine how measures of interestingness can be used to identify and analyse relative contributions of segments of similarity knowledge. We gauge the strengths and weaknesses of knowledge components and discuss future work as well as implications for research in the area.
The support of the Informatics Research Initiative of Enterprise Ireland is gratefully acknowledged.
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Resnick, P., Varian, H.R.: Recommender Systems. Communications of the ACM 40, 56–58 (1997)
Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12, 331–370 (2002)
McJones, P.: Eachmovie Collaborative Filtering Dataset, DEC Systems Research Center, http://www.research.compaq.com/src/eachmovie/ (1997)
Konstan, J.A., Miller, B.N., et al.: Grouplens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40, 77–87 (1997)
Rosenstein, M., Lochbaum, C.: Recommending from Content: Preliminary Results from an E-Commerce Experiment. In: CHI 2000, pp. 291–292. ACM Press, New York (2000)
Smyth, B., Cotter, P.: Personalized Electronic Programme Guides. Artificial Intelligence Magazine 22, 89–98 (2001)
Hayes, C., Cunningham, P., Smyth, B.: A Case-Based View of Automated Collaborative Filtering. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 234–248. Springer, Heidelberg (2001)
Burke, R.: A case-based reasoning approach to collaborative filtering. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 370–379. Springer, Heidelberg (2000)
Sarwar, B., Karypis, G., et al.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International WWW Conference, pp. 285–295. ACM Press, New York (2001)
Sarwar, B., Karypis, G., et al.: Analysis of Recommendation Algorithms for ECommerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158–167. ACM Press, New York (2000)
O’Sullivan, D., Wilson, D., Smyth, B.: Using Collaborative Filtering Data in Casebased Recommendation. In: Haller, S.M., Simmons, G. (eds.) Proceedings of the 15th International FLAIRS Conference, pp. 121–128. AAAI Press, Menlo Park (2002)
O’Sullivan, D., Wilson, D., Smyth, B.: Preserving Recommender Accuracy and Diversity in Sparse Datasets. In: Russell, I., Haller, S. (eds.) Proceedings of the 16th International FLAIRS Conference, pp. 139–144. AAAI Press, Menlo Park (2003)
O’Sullivan, D., Smyth, B., Wilson, D.: In-Depth Analysis of Similarity Knowledge and Metric Contributions to Recommender Performance. In: Proceedings of the 17th International FLAIRS Conference (2004) (in Press)
McKenna, E., Smyth, B.: Competence-guided case-base editing techniques. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 186–197. Springer, Heidelberg (2000)
Agrawal, R., Mannila, H., et al.: Fast Discovery of Association Rules. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. MIT Press, Cambridge (1996)
Goldberg, K., Roeder, T., et al.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval Journal 4, 133–151 (2001)
Foltz, P.W.: Using Latent Semantic Indexing for Information Filtering. In: Conference on Office Information Systems, pp. 40–47. ACM Press, New York (1990)
Honda, K., Sugiura, N., et al.: Collaborative Filtering Using Principal Component Analysis and Fuzzy Clustering. In: Zhong, N., Yao, Y., Ohsuga, S., Liu, J. (eds.) WI 2001. LNCS (LNAI), vol. 2198, pp. 394–402. Springer, Heidelberg (2001)
Tan, P.N., Kumar, V., et al.: Selecting the Right Interestingness Measure for Association Patterns. In: Proceedings of the 8th ACM SIGKDD Conference, pp. 32–41 (2002)
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O’Sullivan, D., Smyth, B., Wilson, D.C. (2004). Analysing Similarity Essence for Case Based Recommendation. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_52
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DOI: https://doi.org/10.1007/978-3-540-28631-8_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22882-0
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