电子商务推荐系统中群体用户推荐问题研究

中国管理科学 ›› 2013, Vol. 21 ›› Issue (3): 153-158.

电子商务推荐系统中群体用户推荐问题研究

梁昌勇^1,2, 冷亚军^1,2, 王勇胜³, 戚筱雯^1,2

1. 合肥工业大学管理学院, 安徽合肥 230009;
2. 过程优化与智能决策教育部重点实验室, 安徽合肥 230009;
3. 东北电力大学建筑工程学院, 吉林吉林 132012

收稿日期:2011-06-28 修回日期:2012-06-21 出版日期:2013-06-30 发布日期:2013-06-20
基金资助:
高等学校博士学科点专项科研基金项目(20110111110006);教育部人文社会科学研究青年基金项目(09YJC630055)

Research on Group Recommendation in E-commerce Recommender Systems

LIANG Chang-yong^1,2, LENG Ya-jun^1,2, WANG Yong-sheng³, QI Xiao-wen^1,2

1. School of Management, Hefei University of Technology, Hefei 230009, China;
2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China;
3. School of Civil and Architecture, Northeast Dianli University, Jilin 132012, China

Received:2011-06-28 Revised:2012-06-21 Online:2013-06-30 Published:2013-06-20

摘要/Abstract

摘要： 尽管传统的电子商务推荐系统在个体用户推荐方面取得了巨大成功,但它并不适用于向群体用户进行推荐。随着虚拟社区中群体用户的不断增加,构建群体推荐系统,向群体用户提供个性化推荐,减少他们搜集信息所耗费的时间和精力显得越来越重要。基于此,本文提出了一种新颖的推荐方法—结合领域专家法的群体用户推荐算法。该算法以基于项目的协同过滤技术为基础,根据群体成员间的相互作用确定群体偏好,由群体偏好产生推荐,推荐过程中存在的成员未评分项采用领域专家法进行预测填充,此外本文算法还考虑了成员间相似关系对推荐质量的影响。实验结果表明了本文算法的有效性。

关键词: 电子商务推荐系统, 群体用户推荐, 协同过滤, 领域专家法

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

中图分类号:

TP311

梁昌勇, 冷亚军, 王勇胜, 戚筱雯. 电子商务推荐系统中群体用户推荐问题研究[J]. 中国管理科学, 2013, 21(3): 153-158.

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.

参考文献

[1] Lee T Q, Park Y,Park Y T.An empirical sudy on effectiveness of temporal information as implicit ratings [J]. Expert Systews with Applications,2009,36(2):1315-1321.
[2] Linden G, Smith B, York J. Amazon.com recommendations: item-to-item collaborative filtering [J]. IEEE Internet Computing, 2003, 7(1): 76-80.
[3] Russell S, Yoon V. Applications of wavelet data reduction in a recommender system [J]. Expert System with Applications, 2008, 34(4): 2316-2325.
[4] Shang Mingsheng, Jin Cihang, Zhou Tao,et al. Collaborative filtering based on multi-channel diffusion [J]. Physica A, 2009, 388(23): 4867-4871.
[5] 豆瓣网站[EB/OL]. http://www.douban.com.
[6] McCarthy K, Salamó M, Coyle L, et al. Group recommender systems: A critiquing based approach [C]//Proceedings of the 11th international conference on intelligent user interfaces.New York:ACM,2006:267-269.
[7] McCarthy J F, Anagnost T D. MusicFX: An arbiter of group preferences for computer supported collaborative workouts [C]//Proceedings of the 1998 ACM conference on computer-supported cooperative work. New York:ACM,1998: 363-372.
[8] O’Connor M, Cosley D, Konstan JA, et al. PolyLens: A recommender system for groups of users[C]// Proceedings of the 7th European conference on computer supported cooperative work. Norwell:Kluwer Academic,2001.
[9] Jameson A. More than the sum of its members: challenges for group recommender systems [C]// Proceedings of the working conference on advanced visual interfaces.New York:ACM, 2004.
[10] Chen Yenliang,Cheng Lichen,Chuang Chingnan. A group recommendation system with consideration of interactions among group members [J]. Expert Systems with Applications, 2008, 34(3): 2082-2090.
[11] Herlocker J, Konstan J, Terveen L, et al. Evaluating collaborative filtering recommender systems [J]. ACM Transactions on Information Systems, 2004, 22 (1): 5-53.
[12] Lee S K, Cho Y H, Kim S H. Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations [J]. Information Sciences, 2010, 180(11): 2142-2155.
[13] 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法 [J].软件学报, 2003, 14(9): 1621-1628.
[14] Sarwar B M. Sparsity, scalability, and distribution in recommender systems[D]. Minneapolis: University of Minnesota, 2001.
[15] Lee J S, Olafsson S. Two-way cooperative prediction for collaborative filtering recommendations[J]. Expert Systems with Applications, 2009, 36: 5353-5361.
[16] 当当网[EB/OL]. http://www.dangdang.com.
[17] 李聪,梁昌勇,马丽.基于领域最近邻的协同过滤推荐算法[J].计算机研究与发展,2008,45(9):1532-1538.
[18] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions [J]. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[19] Ahn H J. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem [J]. Information Sciences, 2008, 178(1): 37-51.
[20] Masthoff J, Gatt A. In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems [J]. User Modeling and User-Adapted Interaction, 2006, 16(3-4): 281-319.
[21] Movie Lens website[EB/OL]. http://movielens.umn.edu.
[22] 郁雪,李敏强.基于PCA-SOM的混合协同过滤模型[J].系统工程理论与实践, 2010, 30(10): 1850-1854.
[23] MacQueen J. Some methods for classification and analysis of multivariate observations[C]//Proceedings of the 5th Berkeley symposium on mathematical statistics and probability,1967:281-297.