  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.
