ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (1): 113-124.doi: 10.7544/issn1000-1239.2018.20160704

• 人工智能 • 上一篇    下一篇

融合用户社会地位和矩阵分解的推荐算法

余永红1,2,3,高阳2,3,王皓2,3,孙栓柱4   

  1. 1(南京邮电大学通达学院 南京 210003);2(计算机软件新技术国家重点实验室(南京大学) 南京 210023);3(江苏省软件新技术与产业化协同创新中心(南京大学) 南京 210023);4(江苏方天电力技术有限公司 南京 211100) (yuyh.nju@gmail.com)
  • 出版日期: 2018-01-01
  • 基金资助: 
    国家自然科学基金项目(61432008,61503178,61403208);江苏省自然科学基金项目(BK20150587);江苏省高等学校自然科学研究项目(17KJB520028)

Integrating User Social Status and Matrix Factorization for Item Recommendation

Yu Yonghong1,2,3, Gao Yang2,3, Wang Hao2,3, Sun Shuanzhu4   

  1. 1(Tongda College, Nanjing University of Posts and Telecommunications, Nanjing 210003);2(State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210023);3(Collaborative Innovation Center of Novel Software Technology and Industrialization (Nanjing University), Nanjing 210023);4(Jiangsu Frontier Electric Technology Co. LTD., Nanjing 211100)
  • Online: 2018-01-01

摘要: 随着社交网络服务的日益流行,社交网络平台为推荐算法提供了丰富的额外信息.假设朋友之间共享更多的共同偏好并且用户往往易于接受来自朋友的推荐,越来越多的推荐系统利用社交网络中用户之间的信任关系来改进传统推荐算法的性能.然而,现有基于社交网络推荐算法忽略了2个问题:1)在不同的领域中,用户信任不同的朋友;2)由于用户在不同的领域内具有不同的社会地位,因此,用户在不同的领域内受朋友的影响程度是不同的.首先利用整体的社交网络结构信息和用户的评分信息推导特定领域社交网络结构,然后利用PageRank算法计算用户在特定领域的社会地位,最后提出了一种融合用户社会地位信息的矩阵分解推荐算法.在真实数据集上的实验结果表明:融合用户地位信息的矩阵分解推荐算法的性能优于传统的基于社交网络推荐算法.

关键词: 用户社会地位, 矩阵分解, 推荐算法, PageRank算法, 社交网络

Abstract: With the increasing popularity of online social network services, social networks platforms provide rich information for recommender systems. Based on the assumption that friends share more common interests than non-friends and users tend to accept the item recommendations from friends, more and more recommender systems utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of the existing social-network-based recommendation algorithms ignore the following problems: 1) in different domains, users tend to trust different friends; 2) the degree of influence that a user is affected by their trusted friends is different in different domains since the user has different social status in different domains. In this paper, we first infer domain-specific social trust relation networks based on original users’ rating information and social network information, and then compute each user’s social status by leveraging PageRank algorithm for each specific domain. Finally, we propose a novel recommendation algorithm by integrating users’ social status with matrix factorization model. Experimental results on real-world dataset show that our proposed approach outperforms traditional social-network-based recommenda-tion algorithms.

Key words: user social status, matrix factorization, recommendation algorithm, PageRank algorithm, social network

中图分类号: