User Rating Prediction Based on Trust-Driven Probabilistic Matrix Factorization

被引：0
|
作者：
Du D.-F. [1 ,2 ]
Xu T. [1 ,2 ]
Lu Y.-N. [1 ,2 ]
Guan C. [1 ,2 ]
Liu Q. [1 ,2 ]
Chen E.-H. [1 ,2 ]
机构：
[1] Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei
[2] School of Computer Science and Technology, University of Science and Technology of China, Hefei
来源：
Ruan Jian Xue Bao/Journal of Software | 2018年 / 29卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Clustering analysis; Probabilistic matrix factorization; Recommendation system; Trust relationship;
D O I：
10.13328/j.cnki.jos.005322
中图分类号：
学科分类号：
摘要：
The development of Internet has brought convenience to the public, but also troubles users in making choices among enormous data. Thus, recommender systems based on user understanding are urgently in need. Different from the traditional techniques that usually focus on individual users, the social-based recommender systems perform better with integrating social influence modeling to achieve more accurate user profiling. However, current works usually generalize influence in simple mode, while deep discussions on intrinsic mechanism have been largely ignored. To solve this problem, this paper studies the social influence within users who affects both rating and user attributes, and then proposes a novel trust-driven PMF (TPMF) algorithm to merge these two mechanisms. Furthermore, to deal with the task that different user should have personalized parameters, the study clusters users according to rating correlation and then maps them to corresponding weights, thereby achieving the personalized selection of users' model parameters. Comprehensive experiments on open data sets validate that TPMF and its derivation algorithm can effectively predict users' rating compared with several state of the art baselines, which demonstrates the capability of the presented influence mechanism and technical framework. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：3747 / 3763
页数：16
相关论文
共 30 条
  • [1] Liu Q., Zeng X., Zhu H.S., Et al., Mining indecisiveness in customer behaviors, Proc. of the 2015 IEEE Int'l Conf. on Data Mining (ICDM), pp. 281-290, (2015)
  • [2] Ma H., King I., Lyu M.R., Learning to recommend with social trust ensemble, Proc. of the 32nd Int'l ACM SIGIR Conf. on Researchand Development in Information Retrieval, pp. 203-210, (2009)
  • [3] Jamali M., Ester M., A transitivity aware matrix factorization model for recommendation in social networks, Proc. of the 22th Int'l Joint Conf. on Artificial Intelligence, pp. 2644-2649, (2011)
  • [4] Liu Q., Ge Y., Li Z., Et al., Personalized travel package recommendation, Proc. of the 2011 IEEE 11th Int'l Conf. on Data Mining (ICDM), pp. 407-416, (2011)
  • [5] Lops P., De Gemmis M., Semeraro G., Content-Based Recommender Systems: State of the Art and Trends, pp. 73-105, (2011)
  • [6] Schafer J., Frankowski D., Herlocker J., Et al., Collaborative filtering recommender systems, pp. 291-324, (2007)
  • [7] Ricci F., Rokach L., Shapira B., Introduction to Recommender Systems Handbook, (2011)
  • [8] Guo L., Ma J., Chen Z.M., Trust strength aware social recommendation method, Journal of Computer Research and Development, 50, 9, pp. 1805-1813, (2013)
  • [9] Li Y.S., Song M.N., Hai-Hong E., Et al., Social recommendation algorithm fusing user interest social network, Journal of China Universities of Posts & Telecommunications, 21, 14, pp. 26-33, (2014)
  • [10] Ma H., Yang H., Lyu M.R., Et al., Sorec: Social recommendation using probabilistic matrix factorization, Proc. of the 17th ACM Conf. on Information and Knowledge Management, pp. 931-940, (2008)
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