Feature reduction for product recommendation in internet shopping malls | Zendy

Hyung Jun Ahn | Zendy; Jong Woo Kim | Zendy

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Feature reduction for product recommendation in internet shopping malls

Author(s) -

Hyung Jun Ahn,

Jong Woo Kim

Publication year - 2006

Publication title -

international journal of electronic business

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.142

H-Index - 8

eISSN - 1741-5063

pISSN - 1470-6067

DOI - 10.1504/ijeb.2006.011329

Subject(s) - feature (linguistics) , computer science , product (mathematics) , the internet , reduction (mathematics) , business , world wide web , advertising , mathematics , philosophy , linguistics , geometry

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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