Abstract
We propose a new personalized recommendation technique, which dynamically recommends products based on user behavior patterns for E-commerce. It collects and analyzes user behavior patterns from XML-based E-commerce sites using the PRML (Personalized Recommendation Markup Language) approach. The collected information is saved as PRML instances and an individual user profile is built from the PRML instances of the user using a CBR (Case-Based Reasoning) learning technique. When a new product is introduced, the system compares, for a user, the preference information saved in the user profile and the information about the new product and produces a recommendation that best fits the user preference.
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References
Eirinaki, M., Vazirgiannis, M.: Web Mining for Web Personalization. ACM Transaction on Internet Technology 3(1), 1–27 (2003)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommender Algorithms for E-Commerce. In: Proceedings of ACM Conference on E-Commerce, pp. 158–167 (2000)
Pazzani, M.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligent Review, 394–408 (1999)
Renick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of ACM Conference on Electronic Commerce, pp. 175–186 (1994)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sati, M.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In: Proceedings of ACM SIGIR Workshop on Recommender Systems (1999)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: Proceedings of WWW Conference, pp. 285–295 (2001)
Schafer, J.B., Konstan, J., Riedl, J.: Recommender Systems in E-Commerce. In: Proceedings of ACM Conference on Electronic Commerce, pp. 158–166 (1999)
Claypool, M., Brown, D., Le, P., Waseda, M.: Inferring User Interest. Technical Report WPI-CS-TR-01-97 (2001)
Spiliopoulou, M., Pohle, C., Faulstich, L.C.: Improving the Effectiveness of a Web Site with Web Usage Mining. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 142–162. Springer, Heidelberg (2000)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M., Sun, Y., Wilshire, J.: Discovery of Aggregate Usage Profiles for Web Personalization. In: Proceedings of WEBKDD (2000)
Mobasher, B., Cooley, R., Srivastava, J.: Automatic Personalization based on Web Usage Mining. Communications of ACM, 142–151 (2000)
Mun, H.J., Ok, S.H., Woo, Y.T.: An Automatic Rating Technique Based on XML Document. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 424–427. Springer, Heidelberg (2002)
Kim, Y.J., Ok, S.H., Woo, Y.T.: A Case-Based Recommender System using Implicit Rating Techniques. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 522–526. Springer, Heidelberg (2002)
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Kim, Y.J., Mun, H.J., Lee, J.Y., Woo, Y.T. (2006). A Personalized Recommendation System Based on PRML for E-Commerce. In: Wiedermann, J., Tel, G., Pokorný, J., Bieliková, M., Štuller, J. (eds) SOFSEM 2006: Theory and Practice of Computer Science. SOFSEM 2006. Lecture Notes in Computer Science, vol 3831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11611257_34
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DOI: https://doi.org/10.1007/11611257_34
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