차원 감소 기법을 이용한 전자 상거래 추천 시스템 Development of a Recommender System for E-Commerce Sites Using a Dimensionality Reduction Technique

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The recommender system is a typical software solution for personalized services which are now popular in e-commerce sites. Most of the existing recommender systems are based on customers’ explicit rating data on items (e.g., ratings on movies), and it is only recently that recommender systems based on implicit ratings have been proposed as a better alternative. Implicit ratings of a customer on those items that are clicked but not purchased can be inferred from the customer’s navigational and behavioral patterns. In this article, a dimensionality reduction (DR) technique is newly applied to the implicit rating-based recommender system, and its effectiveness is assessed using an experimental e-commerce site. The experimental results indicate that the performance of the proposed approach is superior or at least similar to the conventional collaborative filtering (CF)-based approach unless the number of recommended products is ‘large.’ In addition, the proposed approach requires less memory space and is computationally more efficient.
Publisher
대한산업공학회
Issue Date
2010-09
Language
Korean
Citation

대한산업공학회지, v.36, no.3, pp.193 - 202

ISSN
1225-0988
URI
http://hdl.handle.net/10203/97324
Appears in Collection
IE-Journal Papers(저널논문)
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