Abstract
This paper proposes a recommendation methodology to help customers find the products they would like to purchase in a Web retailer. The methodology is based on collaborative filtering, but to overcome the sparsity issue, we employ an implicit ratings approach based on Web usage mining. Furthermore to address the scalability issue, a dimension reduction technique based on product taxonomy together with association rule mining is used. The methodology is experimentally evaluated on real Web retailer data and the results are compared to those of typical collaborative filtering. Experimental results show that our methodology provides higher quality recommendations and better performance, so it could be a promising marketer assistant tool for the Web retailer.
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© 2005 Springer-Verlag Berlin Heidelberg
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Cho, Y.H., Kim, J.K., Ahn, D.H. (2005). A Personalized Product Recommender for Web Retailers. In: Baik, DK. (eds) Systems Modeling and Simulation: Theory and Applications. AsiaSim 2004. Lecture Notes in Computer Science(), vol 3398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30585-9_33
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DOI: https://doi.org/10.1007/978-3-540-30585-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24477-6
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