计算机工程与应用2019，Vol.55Issue(10)：9-15,7.DOI:10.3778/j.issn.1002-8331.1901-0098
融合偏好度与网络结构的推荐算法
Incorporating User Preferences and Network Structure for Recommendation
摘要
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 collabora-tive 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 diver-sified 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.关键词
推荐系统/偏好度/多样性/网络结构Key words
recommender system/user preferences/diversity/network structure分类
信息技术与安全科学引用本文复制引用
黄继婷,陈建兵,陈平华..融合偏好度与网络结构的推荐算法[J].计算机工程与应用,2019,55(10):9-15,7.基金项目
天津市普通高校本科教学质量与教学改革研究计划项目(No.171005704B). （No.171005704B）
