Trust Evolution: Modeling and Its Applications

被引:20
|
作者
Tang, Jiliang [1 ]
Gao, Huiji [1 ]
Das Sarma, Atish [3 ]
Bi, YingZhou [2 ]
Liu, Huan [1 ]
机构
[1] Arizona State Univ, Dept Comp Sci & Engn, Tempe, AZ 85281 USA
[2] Guangxi Teachers Educ Univ, Sci Comp & Intelligent Informat Proc GuangXi High, Nanning 530023, Peoples R China
[3] EBay Inc, EBay Res Labs, San Jose, CA 95125 USA
基金
美国国家科学基金会;
关键词
Multi-faceted trust; preference-based trust evolution; user preference; social recommendation; trust prediction;
D O I
10.1109/TKDE.2014.2382576
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Trust plays a crucial role in helping online users collect reliable information and it has gained increasing attention from the computer science community in recent years. Traditionally, research about online trust assumes static trust relations between users. However, trust, as a social concept, evolves as people interact. Most existing studies about trust evolution are from sociologists in the physical world while little work exists in an online world. Studying online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this work, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we identify the differences of trust evolution study in physical and online worlds and propose a framework, eTrust, to study trust evolution using online data from passive observation in the context of product review sites by exploiting the dynamics of user preferences. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as trust prediction, rating prediction and ranking evolution.
引用
收藏
页码:1724 / 1738
页数:15
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