Sparse Trust Data Mining

被引：2

作者：

Nie, Pengli ^{[1
]}

Xu, Guangquan ^{[1
,2
]}

Jiao, Litao ^{[2
]}

Liu, Shaoying ^{[3
]}

Liu, Jian ^{[1
]}

Meng, Weizhi ^{[4
]}

Wu, Hongyue ^{[5
]}

Feng, Meiqi ^{[1
]}

Wang, Weizhe ^{[1
]}

Jing, Zhengjun ^{[6
]}

Zheng, Xi ^{[7
]}

机构：

[1] Tianjin Univ, Coll Intelligence & Comp, Tianjin Key Lab Adv Networking TANK, Tianjin 300350, Peoples R China

[2] Qingdao Huanghai Univ, Sch Big Data, Qingdao 266000, Peoples R China

[3] Hiroshima Univ, Sch Informat & Data Sci, Higashihiroshima 7398511, Japan

[4] Tech Univ Denmark, Dept Appl Math & Comp Sci, DK-2800 Copenhagen, Denmark

[5] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China

[6] Jiangsu Univ Technol, Sch Comp Engn, Changzhou 213001, Peoples R China

[7] Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia

来源：

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY | 2021年 / 16卷

基金：

美国国家科学基金会;

关键词：

Data mining; Sparse matrices; Data models; Computational modeling; Collaboration; Big Data; Recommender systems; Anti-sparsification; recommendation system; sparse trust; trust model; RECOMMENDER SYSTEM; CLOUD; MODEL;

D O I：

10.1109/TIFS.2021.3109412

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

引用

页码：4559 / 4573

页数：15

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