Feature reduction for product recommendation in internet shopping malls
Hyung Jun Ahn, Jong Woo Kim · International Journal of Electronic Business · 2006
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.