关键词: 社会化推荐系统, 社交关系, 协同过滤, 中心节点

Abstract: At present, the research of social recommender systems mainly focuses on the construction of models based recommendation algorithms. However, the latent factors and the social information involved in the algorithms may bring uncertainty to the recommendation quality. To explore this uncertainty, this paper explores the impact of the social relation in the model-based social recommender systems. The experiments remove edges or nodes in the relation network proportionally, and then evaluate the recommendations. The results show that, with the increase of the relation density, the performance of the recommendation algorithms is boosted, and the central nodes make huge difference. Hence the social relation information should be collected much more during building the model-based social recommender systems. Moreover, processing data according to the importance of the nodes in the recommendation can help to enhance the recommendation performance and reduce the impact of noncentral nodes.

Key words: social recommender system, social relation, collaborative filter, central nodes