Recommendation Model Combining Implicit Influence of Trust with Trust Degree
ZHANG Binqi1, REN Lifang2, WANG Wenjian1,3
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006; 2. School of Information, Shanxi University of Finance and Economics, Taiyuan 030006; 3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract：Some methods alleviate the cold start problem in recommender systems by combining traditional recommendation techniques and social information. However, the effect is poor due to the less available social information. Therefore, a recommendation model combining implicit influence of trust and trust degree(RIITD) is proposed in this paper. On the premise of introducing the trust relationship in social information, both the explicit behavior data of the user in the trust relationship and the implicit influence of trust relationship, such as the potential feature vector of trusted users, are taken into account to obtain the preference characteristics of cold start users. Consequently, the problem of inaccurate recommen-dation for the cold start users caused by less social information is alleviated. Moreover, the compre-hensive trust degree is introduced to reflect the different social influences between the target user and the trusted users, make the trusted users play a positive impact and improve the performance of the recommender system. Experiments on 3 commonly used datasets show that the proposed method can achieve high accuracy.
[1] 冷亚军,陆青,梁昌勇.协同过滤推荐技术综述.模式识别与人工智能, 2014, 27(8): 720-734. (LENG Y J, LU Q, LIANG C Y. Survey of Recommendation Based on Collaborative Filtering. Pattern Recognition and Artificial Intelligence, 2014, 27(8): 720-734.) [2] 徐堃,朱小柯,荆晓远.基于改进协同过滤的个性化Web服务推荐方法研究.计算机技术与发展, 2018, 28(1): 64-68. (XU K, ZHU X K, JING X Y. Research on Personalized Web Service Recommendation Based on Improved Collaborative Filtering. Computer Technology and Development, 2018, 28(1): 64-68.) [3] 张宏丽,白翔宇,李改梅.利用最近邻域推荐且结合情境感知的个性化推荐算法.计算机科学, 2019, 46(4): 235-240. (ZHANG H L, BAI X Y, LI G M. Personalized Recommendation Algorithm Based on Recent Neighborhood Recommendation and Combined with Context Awareness. Computer Science, 2019, 46(4): 235-240.) [4] MAO X X, ZHAO X, LIN J, et al. Utilizing Multi-source Data in Popularity Prediction for Shop-Type Recommendation. Knowledge-Based Systems, 2019, 165: 253-267. [5] KOREN Y. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model // Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2008: 426-434. [6] 杨武,唐瑞,卢玲.基于内容的推荐与协同过滤融合的新闻推荐方法.计算机应用, 2016, 36(2): 414-418. (YANG W, TANG R, LU L. News Recommendation Method by Fusion of Content-Based Recommendation and Collaborative Fil-tering. Journal of Computer Applications, 2016, 36(2): 414-418.) [7] XIE X L, PANG S T, CHEN J L. Hybrid Recommendation Model Based on Deep Learning and Stacking Integration Strategy. Intelligent Data Analysis, 2020, 24(6): 1329-1344. [8] 王佳蕾,郭耀,刘志宏.基于社交网络信任关系的服务推荐方法.计算机科学, 2018, 45(11A): 402-408. (WANG J L, GUO Y, LIU Z H. Service Recommendation Method Based on Social Network Trust Relationships. Computer Science, 2018, 45(11A): 402-408.) [9] GUO G B, ZHANG J, YORKE-SMITH N. Leveraging Multiviews of Trust and Similarity to Enhance Clustering-Based Recommender Systems. Knowledge-Based Systems, 2015, 74: 14-27. [10] MA H, YANG H X, LYU M R, et al. SoRec: Social Recommendation using Probabilistic Matrix Factorization // Proc of the 17th ACM Conference on Information and Knowledge Management. New York, USA: ACM, 2008: 931-940. [11] MA H, KING I, LYU M R. Learning to Recommend with Social Trust Ensemble // Proc of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2009: 203-210. [12] MA H, ZHOU D Y, LIU C, et al. Recommender Systems with Social Regularization // Proc of the 4th ACM International Confe-rence on Web Search and Data Mining. New York, USA: ACM, 2011: 287-296. [13] JAMALI M, ESTER M. A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks // Proc of the 4th ACM Conference on Recommender Systems. New York, USA: ACM, 2010: 135-142. [14] YANG B, LEI Y, LIU J M, et al. Social Collaborative Filtering by Trust. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1633-1647. [15] TANG J L, HU X, GAO H J, et al. Exploiting Local and Global Social Context for Recommendation // Proc of the 23rd International Joint Conference on Artificial Intelligence, San Mateo. USA: Morgan Kaufmann, 2013: 2712-2718. [16] MA H. An Experimental Study on Implicit Social Recommendation // Proc of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2013: 73-82. [17] YAO W L, HE J, HUANG G Y, et al. Modeling Dual Role Pre-ferences for Trust-Aware Recommendation // Proc of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2014: 975-978. [18] GUO G B, ZHANG J, YORKE-SMITH N. TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings // Proc of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI, 2015: 123-129. [19] XIONG F, SHEN W H, CHEN H S, et al. Exploiting Implicit Influence from Information Propagation for Social Recommendation. IEEE Transactions on Cybernetics, 2020, 50(10): 4186-4199. [20] AHMADIAN S, JOORABLOO N, JALILI M, et al. A Social Re-commender System Based on Reliable Implicit Relationships. Knowledge-Based Systems, 2020, 192. DOI: 10.1016/j.knosys.2019.105371. [21] LIU P, ZHANG L M, GULLA J A. Real-Time Social Recommendation Based on Graph Embedding and Temporal Context. International Journal of Human-Computer Studies, 2019, 121: 58-72.
2022, Vol. 35 
