Comparison of Product and Customer Feature Selection Methods for Content-based Recommendation in Internet Storefronts 


Vol. 13,  No. 2, pp. 279-286, Apr.  2006
10.3745/KIPSTD.2006.13.2.279


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  Abstract

One of the widely used methods for product recommendation in Internet storefronts is matching product features against target customer profiles. When using this method, it’s 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 utilize a dataset collected from a virtual shopping experiment in a Korean Internet book shopping mall to compare several popular methods from other disciplines for selecting features for product recommendation: the vector-space model, TFIDF(Term Frequency-Inverse Document Frequency), the mutual information method, and the singular value decomposition(SVD). The application of SVD showed the best performance in the analysis results.

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[IEEE Style]

H. J. Ahn and J. W. Kim, "Comparison of Product and Customer Feature Selection Methods for Content-based Recommendation in Internet Storefronts," The KIPS Transactions:PartD, vol. 13, no. 2, pp. 279-286, 2006. DOI: 10.3745/KIPSTD.2006.13.2.279.

[ACM Style]

Hyung Jun Ahn and Jong Woo Kim. 2006. Comparison of Product and Customer Feature Selection Methods for Content-based Recommendation in Internet Storefronts. The KIPS Transactions:PartD, 13, 2, (2006), 279-286. DOI: 10.3745/KIPSTD.2006.13.2.279.

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