Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering As the core of recommender system, collaborative filtering (CF) models the affinity between a user and an item from historical useritem interactions, such as clicks, purchases, and so on. Benefited from the strong representation power, neural networks have recently revolutionized the recommendation research, setting up a new standard for CF. However, existing neural recommender models do not explicitly consider the correlations among embedding dimensions, making them less effective in modeling the interaction function between users and items. In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF. We propose a novel and general neural collaborative filtering framework, namely ConvNCF, which is featured with two designs: 1) applying outer product on user embedding and item embedding to explicitly model the pairwise correlations between embedding dimensions, and 2) employing convolutional neural network above the outer product to learn the highorder correlations among embedding dimensions. To justify our proposal, we present three instantiations of ConvNCF by using different inputs to represent a user and conduct experiments on two realworld datasets. Extensive results verify the utility of modeling embedding dimension correlations with ConvNCF, which outperforms several competitive CF methods.

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.