Abstract
Collaborative filtering is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability. This paper proposes a recommendation methodology based on Web usage mining and SVD (Singular Value Decomposition) to enhance the recommendation quality and the system performance of current collaborative filtering-based recommender systems. Web usage mining populates the rating database by tracking customers’ shopping behaviors on the Web, so leading to better quality recommendations. SVD is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real Web retailer data show that the proposed methodology provides higher quality recommendations and better performance than other recommendation methodologies.
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References
Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proc. 15th International Conference on Machine Learning, pp. 46–54 (1998)
Cho, Y.H., Kim, J.K., Kim, S.H.: A personalized recommender system based on Web usage mining and decision tree induction. Expert Systems with Applications 23, 329–342 (2002)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proc. ACM SIGIR 1999 Workshop on Recommender Systems, Berkeley, CA (1999)
Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems 1, 5–32 (1999)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proc. Conference on Research and Development in Information Retrieval, pp. 230–237 (1999)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proc. ACM Conference on Human Factors in Computing Systems, pp. 194–201
Kanth, K.V., Agrawal, D., Abbadi, A.E., Singh, A.: Dimensionality Reduction for Similarity Searching in Dynamic Databases. Computer Vision and Image Understanding 75, 59–72 (1999)
Kim, J.K., Cho, Y.H., Kim, W.J., Kim, J.R., Suh, J.Y.: A personalized recommendation procedure for Internet shopping support. Electronic Commerce Research and Applications 1, 301–313 (2002)
Lee, J., Podlaseck, M., Schonberg, E., Hoch, R.: Visualization and analysis of clickstream data of online stores for understanding Web merchandising. Data Mining and Knowledge Discovery 5, 59–84 (2001)
Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery 6, 83–105 (2001)
Menasce, D.A., Almeida, V.A., Fonseca, R., Mendes, M.A.: A methodology for workload characterization of e-commerce sites. In: Proc. ACM E-Commerce, pp. 119–128 (1999)
Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on Web usage mining. Communications of the ACM 43, 142–151 (2000)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens an open architecture for collaborative filtering of netnews. In: Proc. ACM 1994 Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)
Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Application of dimensionality reduction in recommender system – a case study. In: Proc. ACM WebKDD 2000 Workshop (2000)
Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proc. ACM E-Commerce, pp. 158–167 (2000)
Shardanand, U., Maes, P.: Social information filtering algorithms for automating word of mouth. In: Proc. Conference on Human factors in Computing Systems, pp. 210–217 (2000)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: discovery and applications of usage patterns fromWeb data. SIGKDD Explorations 1, 1–12 (2000)
VanderMeer, D., Dutta, K., Datta, A.: Enabling scalable online personalization on the Web. In: Proc. ACM E-Commerce, pp. 185–196 (2000)
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Kim, J.K., Cho, Y.H. (2003). Using Web Usage Mining and SVD to Improve E-commerce Recommendation Quality. In: Lee, J., Barley, M. (eds) Intelligent Agents and Multi-Agent Systems. PRIMA 2003. Lecture Notes in Computer Science(), vol 2891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39896-7_8
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DOI: https://doi.org/10.1007/978-3-540-39896-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20460-2
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