Joint Neural Collaborative Filtering for Recommender Systems. We propose a JNCF method for recommender systems. The JNCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a useritem rating matrix. Deep interaction modeling captures nonlinear useritem interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. JNCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization, which takes both implicit and explicit feedback, pointwise and pairwise loss into account. Experiments on several realword datasets show significant improvements of JNCF over stateoftheart methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR10. NDCG10 improvements are 12.42%, 14.24% and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of JNCF. Our experiments show that the JNCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to stateoftheart baselines.

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.