Original scientific paper
https://doi.org/10.1080/00051144.2020.1715590
Group recommendation based on hybrid trust metric
Haiyan Wang
; School of Computer Science, Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts & Telecommunications, Nanjing, People’s Republic of China
Dongdong Chen
; School of Computer Science, Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts & Telecommunications, Nanjing, People’s Republic of China
Jiawei Zhang
; School of Computer Science, Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts & Telecommunications, Nanjing, People’s Republic of China
Abstract
Group recommendation is a special service type which has the ability to satisfy a group’s common interest and find the preferred items for group users. Deep mining of trust relationship between group members can contribute to the improvement of accuracy during group recommendation. Most of the existing trust-based group recommendation methods pay little attention to the diversity of trust sources, resulting in poor recommendation accuracy. To address the problem above, this paper proposes a group recommendation method based on a hybrid trust metric (GR-HTM). Firstly, GR-HTM creates an attribute trust matrix and a social trust matrix based on user attributes and social relationships, respectively. Secondly, GR-HTM accomplishes a hybrid trust matrix based on the integration of these two matrices with the employment of the Tanimoto coefficient. Finally, GR-HTM calculates weights for each item in the hybrid trust matrix based on
weighted-meanlist and proceeds to group recommendation with a given trust threshold. Simulation experiments demonstrate that the proposed GR-HTM has better performance for group recommendation in accuracy and effectiveness.
Keywords
Hybrid trust metric; Tanimoto coefficient; group recommendation
Hrčak ID:
258407
URI
Publication date:
23.9.2020.
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