电子商务推荐系统中群体用户推荐问题研究

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。

Research on Group Recommendation in E-commerce Recommender Systems

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.