Survey for Trust-aware Recommender Systems: A Deep Learning Perspective

• URL: http://arxiv.org/abs/2004.03774v2
• Date: Tue, 6 Oct 2020 00:45:13 GMT
• Title: Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
• Authors: Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu and Liming Zhu
• Abstract summary: It becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems.
• Score: 48.2733163413522
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that filter untruthful noises (e.g., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items. We focus on the work based on deep learning techniques, an emerging area in the recommendation research.

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