Improved Research on Resource-Allocation Recommendation Algorithm Based on Trust Relationship

被引：0
作者：
Chen L.-J. [1 ,2 ]
Cai S.-M. [1 ,2 ]
Zhang Q.-M. [1 ,2 ]
Zhou T. [1 ,2 ]
Zhang Y.-C. [1 ,2 ]
机构：
[1] Web Sciences Center, University of Electronic Science and Technology of China, Chengdu
[2] Big Data Research Center, University of Electronic Science and Technology of China, Chengdu
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2019年 / 48卷 / 03期
关键词：
Information filtering; Personalized recommendations; Recommender systems; Resource allocation; Social relationship;
D O I：
10.3969/j.issn.1001-0548.2019.03.022
中图分类号：
学科分类号：
摘要：
In recent years, various recommendation methods have been proposed by referring to processes originated in statistical physics, among them the diffusion-based method is an important branch of study. However, these methods were proposed solely based on rating metrics, while the trust relations among users are always ignored. In this paper, we propose a novel information filtering algorithm by introducing users’ social trust relationships into the original diffusion-based method based on the resource-allocation process. Specifically, a tunable parameter is used to scale the resources received by trusted users in the networked resource redistribution process. The objects collected by trusted users will receive more resources. Extensive experiments on the two real-world rating and trust datasets, Epinions and FriendFeed, suggest that the proposed algorithm has better performance than benchmark algorithms in terms of accuracy, diversity, and novelty in the recommendation. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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页码：449 / 455
页数：6
相关论文
共 30 条
  • [21] Yuan J., Zhang Q.M., Gao J., Et al., Promotion and resignation in employee networks, Physica A, 444, pp. 442-447, (2016)
  • [22] Li Y.M., Wu C.T., Lai C.Y., A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship, Decision Support Systems, 55, 3, pp. 740-752, (2013)
  • [23] Ma H., On measuring social friend interest similarities in recommender systems, The 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 465-474, (2014)
  • [24] Ma H., King I., Lyu M.R., Learning to recommend with social trust ensemble, The 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 203-210, (2009)
  • [25] Guo G., Zhang J., Yorke-Smith N., Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems, Knowledge-Based Systems, 74, pp. 14-27, (2015)
  • [26] Shen X., Long H., Ma C., Incorporating trust relationships in collaborative filtering recommender system, Software engineering, The 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 1-8, (2015)
  • [27] Wang X., Liu Y., Zhang G., Et al., Diffusion-based recommendation with trust relations on tripartite graphs, Journal of Statistical Mechanics: Theory and Experiment, 8, (2017)
  • [28] Hanley J.A., Mcneil B.J., The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 1, pp. 29-36, (1982)
  • [29] Zhou T., Lu L., Zhang Y.C., Predicting missing links via local information, European Physical Journal B, 71, 4, pp. 623-630, (2009)
  • [30] Herlocker J.L., Konstan J.A., Terveen L.G., Et al., Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), 22, 1, pp. 5-53, (2004)
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