Feature Reduction for Product Recommendation in Internet Shopping Malls

Authors

Hyung Jun Ahn, Department of Management Systems, Waikato Management School, University of Waikato, New ZealandFollow
Jong Woo Kim, Department of Management, Hanyang University, Seoul, Korea

Document Type

Article

Abstract

One of the widely used methods for product recommendation in Internet shopping malls is matching product features against customers’ profiles. In this method, it is very important to choose suitable set of features for recommendation efficiency and performance, which has, however, not been rigorously researched so far. In this paper, we build a data set collected from a virtual Internet shopping experiment and adapt and apply feature reduction techniques from pattern matching and information retrieval fields to the data to analyze recommendation performance. The analysis shows that the application of SVD (Singular Value Decomposition) can be the best among the applied methods for recommendation performance.

Recommended Citation

Ahn, Hyung Jun and Kim, Jong Woo, "Feature Reduction for Product Recommendation in Internet Shopping Malls" (2005). ICEB 2005 Proceedings (Hong Kong, SAR China). 22.
https://aisel.aisnet.org/iceb2005/22