Attentive Social Recommendation: Towards User And Item Diversities
  Social recommendation system is to predict unobserved user-item rating values
by taking advantage of user-user social relation and user-item ratings.
However, user/item diversities in social recommendations are not well utilized
in the literature. Especially, inter-factor (social and rating factors)
relations and distinct rating values need taking into more consideration. In
this paper, we propose an attentive social recommendation system (ASR) to
address this issue from two aspects. First, in ASR, Rec-conv graph network
layers are proposed to extract the social factor, user-rating and item-rated
factors and then automatically assign contribution weights to aggregate these
factors into the user/item embedding vectors. Second, a disentangling strategy
is applied for diverse rating values. Extensive experiments on benchmarks
demonstrate the effectiveness and advantages of our ASR.
