  As the core of recommender system, collaborative filtering (CF) models the
affinity between a user and an item from historical user-item 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 high-order
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 real-world datasets. Extensive results verify
the utility of modeling embedding dimension correlations with ConvNCF, which
outperforms several competitive CF methods.
