论文标题
细心的社会建议：对用户和项目多样性
Attentive Social Recommendation: Towards User And Item Diversities
论文作者
论文摘要
社交建议系统是通过利用用户用户社交关系和用户项目评级来预测未观察到的用户项目评级值。但是，在文献中，社交建议中的用户/项目多样性并不是很好地利用。特别是，因素间（社会和评级因素）关系以及独特的评分价值需要更多考虑。在本文中，我们提出了一个细心的社会推荐系统（ASR），以从两个方面解决这个问题。首先，在ASR中提出了REC-CONV图网络层来提取社会因素，用户评价和项目评级因素，然后自动分配贡献权重以将这些因子汇总到用户/项目嵌入向量中。其次，将分解策略应用于各种评级值。基准的广泛实验证明了我们ASR的有效性和优势。
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.
