Feature reduction for product recommendation in internet shopping malls Online publication date: Fri, 17-Nov-2006
by Hyung Jun Ahn, Jong Woo Kim
International Journal of Electronic Business (IJEB), Vol. 4, No. 5, 2006
Abstract: One of the widely used methods for product recommendation in internet storefronts is matching product features with target customer profiles. When using this method, it is very important to choose a suitable subset of features for recommendation efficiency and performance, which, however, has not been rigorously researched so far. In this paper, we utilise a dataset collected from a virtual shopping experiment in a Korean internet book shopping mall to compare several popular methods of feature selection from other disciplines for product recommendation: the vector-space model, Term Frequency-Inverse Document Frequency (TFIDF), the Mutual Information (MI) method and the Singular Value Decomposition (SVD). The application of SVD showed the best performance in the analysis results.
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