LAUSR

Given the effectiveness and ease of use, Item-based Collaborative Filtering (ICF) methods have been broadly used in industry and are widely investigated in recent years. The key of ICF lies in the similarity measurement between items, which however is a coarse-grained numerical value that can hardly capture users’ fine-grained preferences toward different attributed aspects of items. In this paper, we propose a model called REDA (Relation Embedding with Dual Attentions) to address this challenge, based on which a new paradigm called Relation-based Collaborative Filtering is designed for high-performance recommendation. REDA is essentially a deep neural network model that employs an item relation embedding scheme for inter-item relations representation. It features in multi-decomposed item embedding with dual-attention refinement and employs a novel relation-wise optimization scheme for end-to-end learning. A relational user embedding is then proposed by aggregating item relation embeddings between all purchased items of a user, which not only profiles users’ fine-grained preferences but also alleviates the data sparsity problem. Extensive experiments are conducted on six real-world datasets and the proposed REDA is shown to outperform ten state-of-the-art methods. In particular, REDA shows great robustness against data and relation sparsity, the ability to learn explainable item aspects, and the potential for large-scale recommendation.