电子商务推荐系统中群体用户推荐问题研究

Abstract

Abstract: Although the traditional e-commerce recommender systems have achieved great success in recommending products to individuals, they are not suitable for group recommendation. As the number of groups increases rapidly in the virtual communities, building group recommender systems to provide personalized services to groups becomes more and more imperative. Therefore, a group recommendation algorithm combined with domain expert imputation is proposed in this paper. The proposed algorithm is designed based on the framework of item-based collaborative filtering. It first identifies group preferences according to every member’s preferences, and then generates recommendations based on the group preferences. Especially, domain expert method is used to impute values for members’ unrated items in the recommendation process. In addition, the proposed algorithm considers the effects of member similarities on recommendation quality. The experimental results show that the proposed algorithm is effective.

Key words: e-commerce recommender systems, group recommendation, collaborative filtering, domain expert imputation

CLC Number:

TP311

LIANG Chang-yong, LENG Ya-jun, WANG Yong-sheng, QI Xiao-wen. Research on Group Recommendation in E-commerce Recommender Systems[J]. Chinese Journal of Management Science, 2013, 21(3): 153-158.

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Research on Group Recommendation in E-commerce Recommender Systems

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