关键词: 推荐系统, 偏好度, 多样性, 网络结构

Abstract: Most of the traditional recommender systems emphasize on accuracy, while ignoring the diversity, and the data set lacks of accessorial information. A recommendation algorithm incorporating user preferences and network structure is proposed. Firstly, the user’s historical feedback data are used to analyze the diversity preferences of the user. Pre-recommended list incorporating the user preferences and BGPR（Bipartite Graph Projection and Ranking）can be obtain. Then collaborative tags contain abundant information about personalized preferences and item contents, and are potential to mine the user’s favorite tags to obtain indirect related recommended list. Finally, the model is employed to generate the final diversified recommendation list. The experimental results show that the proposed method can effectively improve the diversity of recommended list under the premise of ensuring the accuracy rate.

Key words: recommender system, user preferences, diversity, network structure