Multi-criteria assessment of user trust in Social Reviewing Systems with subjective logic fusion

被引:11
作者
Esposito, Christian [2 ]
Galli, Antonio [1 ]
Moscato, Vincenzo [1 ]
Sperli, Giancarlo [1 ]
机构
[1] Univ Naples Federico II, Dept Elect Engn & Informat Technol DIETI, Via Claudio 21, I-80125 Naples, Italy
[2] Univ Salerno, Dept Comp Sci, Via Giovanni Paolo II 132, I-84084 Fisciano, Italy
关键词
Trust; Reputation; Social networks; Multi-criteria decision making; Subjective logic; Trustworthiness assessment; Opinion aggregation; NETWORK; AGGREGATION; MECHANISM; DECISION; SCHEME;
D O I
10.1016/j.inffus.2021.07.012
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
By now people's opinions and actions are more and more strongly influenced by what is posted and shared on the various social networks. Thus, malicious users can purposely manipulate other users posting fake news/reviews. In order to face this challenge, modern online social networks are beginning to adopt tool for user trustworthiness assessment. Current assessment solutions mainly adopt multi-criteria frameworks for user trustworthiness assessment but fail at properly dealing with uncertainty and vagueness in computed/collected scores and aggregating them in a robust manner. In this paper, we propose a larger set of criteria than existing related works, and the use of subjective logic to represent and combine subjective and objective scores. Specifically, several of assessment criteria are introduced for verifying user trust from different point of views (usefulness and quality of user reviews, users' influence/importance in terms of activities and centrality within the social network, time dependent crown consensus investigating aspect-based sentiments and opinions of reviews w.r.t. the majority), aiming at improving accuracy and precision in trust estimation. The available fusion operators in the literature of subjective logic have been compared so as to find the best one fitting the needs of trust estimation. The proposed solution has been implemented and evaluated against public Yelp data-sets so as to prove its effectiveness and efficiency w.r.t. existing related works within the literature.
引用
收藏
页码:1 / 18
页数:18
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