SARC: SplitandRecombine Networks for KnowledgeBased Recommendation Utilizing knowledge graphs (KGs) to improve the performance of recommender systems has attracted increasing attention recently. Existing pathbased methods rely heavily on manually designed metapaths, while embeddingbased methods focus on incorporating the knowledge graph embeddings (KGE) into recommender systems, but rarely model userentity interactions, which can be used to enhance the performance of recommendation. To overcome the shortcomings of previous works, we propose SARC, an embeddingbased model that utilizes a novel SplitAndReCombine strategy for knowledgebased recommendation. Firstly, SARC splits the useritementity interactions into three 2way interactions, i.e., the useritem, userentity and itementity interactions. Each of the 2way interactions can be cast as a graph, and we use Graph Neural Networks (GNN) and KGE to model them. Secondly, SARC recombines the representation of users and items learned from the first step to generates recommendation. In order to distinguish the informative part and meaningless part of the representations, we utilize a gated fusion mechanism. The advantage of our SARC model is that through splitting, we can easily handle and make full use of the 2way interactions, especially the userentity interactions, and through recombining, we can extract the most useful information for recommendation. Extensive experiments on three realworld datasets demonstrate that SARC outperforms several stateoftheart baselines.

Multigraph Convolution Collaborative Filtering Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users and items with the goal of predicting a useru0027s preference for an item based on the similarity of the representations. Techniques range from classic matrix factorization to more recent deep learning based methods. However, we argue that existing methods do not make full use of the information that is available from useritem interaction data and the similarities between user pairs and item pairs. In this work, we develop a graph convolutionbased recommendation framework, named MultiGraph Convolution Collaborative Filtering (MultiGCCF), which explicitly incorporates multiple graphs in the embedding learning process. MultiGCCF not only expressively models the highorder information via a bipartite useritem interaction graph, but integrates the proximal information by building and processing useruser and itemitem graphs. Furthermore, we consider the intrinsic difference between user nodes and item nodes when performing graph convolution on the bipartite graph. We conduct extensive experiments on four publicly accessible benchmarks, showing significant improvements relative to several stateoftheart collaborative filtering and graph neural networkbased recommendation models. Further experiments quantitatively verify the effectiveness of each component of our proposed model and demonstrate that the learned embeddings capture the important relationship structure.