融合用户信任度的概率矩阵分解群组推荐算法

doi:10.19678/j.issn.1000-3428.0059526

摘要/Abstract

摘要： 利用推荐系统进行群组推荐时，群组成员之间的交互关系对推荐结果有很大影响，但传统的群组推荐算法较少考虑用户信任度的重要性，致使社交关系信息不能得到充分利用。在群组融合时考虑群组内用户间的交互关系，提出一种基于用户信任度和概率矩阵的群组推荐算法。在获取用户信任度数据后，使用概率矩阵分解（PMF）算法补全信任度矩阵并进行归一化处理，得到相似度矩阵，同时在后验概率计算过程中加入用户间的信任度因素，通过极大化后验概率获得预测评分。在此基础上，对群组中用户的权重进行归一化处理，使用基于用户交互关系的权重策略融合群组成员偏好，得到最终的推荐结果。在Epinions和FilmTrust数据集上的实验结果表明，该算法可使融合结果更具群组特性，同时提高推荐结果的可靠性和可解释性，且均方根误差和命中率均优于PMF、NeuMF、RippleNet等对比算法。

Abstract: When using the recommendation system for group recommendation, interactions between group members have a great impact on recommendation results, but traditional group recommendation algorithms consider little the importance of user trust, and fail to make full use of social relationship information.Based on probability matrix and user trust, this paper proposes a group recommendation algorithm that considers interactions between users in a group.After obtaining data of user trust, the Probability Matrix Factorization(PMF) algorithm is used to complete the trust matrix, which is then normalized to obtain the similarity matrix.The factor of trust between users is added in the posterior probability calculation process to obtain the prediction score by maximizing the posterior probability.On this basis, the weights of users in the group are normalized, and a weight strategy based on user interactions is used to fuse the preferences of group members to obtain the recommendation result.The experimental results on Epinions dataset and FilmTrust dataset show that the proposed algorithm can make the fusion results to have more group characteristics, and improve the reliability as well as interpretability of the recommendation results.The algorithm displays a lower root mean square error and higher hit rate than PMF, NeuMF, RippleNet and other comparison algorithms.

Key words: group recommendation, preference fusion, Probability Matrix Factorization(PMF), user trust, Collaborative Filtering(CF)

中图分类号:

TP18

宋玉龙, 马文明, 刘彤彤. 融合用户信任度的概率矩阵分解群组推荐算法[J]. 计算机工程, 2022, 48(1): 105-111.

SONG Yulong, MA Wenming, LIU Tongtong. Group Recommendation Algorithm Incorporating User Trust with Probability Matrix Factorization[J]. Computer Engineering, 2022, 48(1): 105-111.

https://www.ecice06.com/CN/Y2022/V48/I1/105

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参考文献

[1] YU W H, ZHANG H D, HE X N, et al.Aesthetic-based clothing recommendation[C]//Proceedings of 2018 World Wide Web Conference.New York, USA:ACM Press, 2018:649-658.
[2] CHAO Y W, AHMED A, BEUTEL A, et al.Recurrent recommender networks[C]//Proceedings of the 10th ACM International Conference on Web Search & Data Mining.New York, USA:ACM Press, 2017:495-503.
[3] COVINGTON P, ADAMS J, SARGIN E.Deep neural networks for YouTube recommendations[C]//Proceedings of 2016 ACM Conference on Recommender Systems.New York, USA:ACM Press, 2016:191-198.
[4] LI X M, XU G Q, TANG M H.Community detection for multi-layer social network based on local random walk[J].Journal of Visual Communication and Image Representation, 2018, 57:91-98.
[5] ARDISSONO L, GOY A, PETRONE G, et al.INTRIGUE:personalized recommendation of tourist attractions for desktop and hand held devices[J].Applied Artificial Intelligence, 2003, 17(8/9):687-714.
[6] 张玉洁, 杜雨露, 孟祥武.组推荐系统及其应用研究[J].计算机学报, 2016, 39(4):745-764. ZHANG Y J, DU Y L, MENG X W.Research on group recommendation system and its application[J].Chinese Journal of Computers, 2016, 39(4):745-764.(in Chinese)
[7] SALAKHUTDINOV R.Probabilistic matrix factorization[C]//Proceedings of International Conference on Neural Information Processing Systems.[S.l.]:Curran Associates, Inc., 2007:1257-1264.
[8] 康雁, 李涛, 李浩, 等.融合知识图谱与协同过滤的推荐模型[J].计算机工程, 2020, 46(12):73-79, 87. KANG Y, LI T, LI H, et al.Recommendation model fusing with knowledge graph and collaborative filtering[J]. Computer Engineering, 2020, 46(12):73-79, 87.(in Chinese)
[9] ROY S B, AMER-YAHIA S, LA A C, et al.Space efficiency in group recommendation[J].The VLDB Journal, 2010, 19(6):877-900.
[10] WANG W, ZHANG G, LU J.Member contribution-based group recommender system[J].Decision Support Systems, 2016(87):80-93.
[11] SHI J, WU B, LIN X.A latent group model for group recommendation[C]//Proceedings of 2015 IEEE International Conference on Mobile Services.Washington D.C., USA:IEEE Press, 2015:233-238.
[12] LI X M, XU G Q, JIAO L T, et al.Multi-layer network community detection model based on attributes and social interaction intensity[J].Computers and Electrical Engineering, 2019, 77:300-313.
[13] YANG B, LEI Y, LIU J, et al.Social collaborative filtering by trust[J].IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(8):1633-1647.
[14] SINGH M, MECHROTRA M.Impact of biclustering on the performance of biclustering based collaborative filtering[J].Expert System with Applications, 2018, 113:443-456.
[15] CHEN H, YIN H, WANG W, et al.PME:projected metric embedding on heterogeneous networks for link prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference.New York, USA:ACM Press, 2018:1-5.
[16] CHEN S, PENG Y.Matrix factorization for recommendation with explicit and implicit feedback[J].Knowledge-Based Systems, 2018, 158:109-117.
[17] CHEN R, HUA Q Y, WANG B, et al.A novel social recommendation method fusing user's social status and homophily based on matrix factorization technique[J].IEEE Access, 2019, 7:18783-18798.
[18] 王根生, 潘方正.融合多元异构信息的矩阵分解推荐算法[J].小型微型计算机系统, 2020, 41(7):1406-1412. WANG G S, PAN F Z.Matrix factorization recommendation algorithms based on multiple heterogeneous information fusion[J].Journal of Chinese Computer Systems, 2020, 41(7):1406-1412.(in Chinese)
[19] LAI C H, LIU D R, LIN C S.Novel personal and group-based trust models in collaborative filtering for document recommendation[J].Information Sciences, 2013, 239(1):31-49.
[20] HE X N, LIAO L Z, ZHANG H W, et al.Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web.New York, USA:ACM Press, 2017:173-182.
[21] WANG H W, ZHANG F Z, WANG J L, et al.RippleNet:propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.New York, USA:ACM Press, 2018:417-426.
[22] YIN H, WANG Q, ZHANG K, et al.Social influence-based group representation learning for group recommendation[C]//Proceedings of the 35th IEEE International Conference on Data Engineering.Washington D.C., USA:IEEE Press, 2019:1-5.

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