ETBRec: a novel recommendation algorithm combining the double influence of trust relationship and expert users

The recommendation system has become the primary tool used by many Internet application platforms to solve the problem of information overload, and it faces issues such as data sparsity, cold start, and scalability. At present, most social recommendation algorithms only consider the influence of the...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2022-01, Vol.52 (1), p.282-294
Main Authors: Duan, Zhenchun, Xu, Weihong, Chen, Yuantao, Ding, Lin
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Influence
Machines
Manufacturing
Mechanical Engineering
Processes
Ratings
Recommender systems
Root-mean-square errors
Trust
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Summary:The recommendation system has become the primary tool used by many Internet application platforms to solve the problem of information overload, and it faces issues such as data sparsity, cold start, and scalability. At present, most social recommendation algorithms only consider the influence of the trust relationship on the user’s feature vector, which indirectly affects the predicted rating, or consider directly trusting friends as neighbor users, which directly affects the predicted rating, but does not consider the direct influence and indirect combining influences to make rating predictions. Therefore, this paper proposes a collaborative filtering recommendation algorithm (ETBRec), which not only considers the trust difference between users but also proposes the definition of experts and considers the direct impact of expert users on prediction ratings and the trustees’ indirect impact of ratings. Among them, the trust difference is realized through trust metrics, including direct trust metrics and indirect trust metrics; the selection of expert users takes into account the user’s degree of trust and user rating attitude; experimental comparisons with various social recommendation algorithms and related recommendation algorithms on the Ciao and Douban datasets. The experimental results show that the ETBRec algorithm performs better on some evaluation indexes such as mean absolute error (MAE) and root mean squared error (RMSE).
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02419-x