Convolutional Adversarial Latent Factor Model for Recommender System

The accuracy of Top-N recommendation task is challenged in the systems with mainly implicit user feedback considered. Adversarial training has presented successful results in identifying real data distributions in various domains (e.g. image processing). Nonetheless, adversarial training applied to recommendation is still challenged especially by interpretation of negative implicit feedback causing it to converge slowly as well as affecting its convergence stability. This is often attributed to high sparsity of the implicit feedback and discrete values characteristic from items recommendation. To face these challenges, we propose a novel model named convolutional adversarial latent factor model (CALF), which uses adversarial training in generative and discriminative models for implicit feedback recommendations. We assume that users prefer observed items over generated items and then apply pairwise product to model the user-item interactions. Additionally, the latent features become input data of our convolutional neural network (CNN) to learn correlations among embedding dimensions. Finally, Rao-Blackwellized sampling is adopted to deal with the discrete values optimizing CALF and stabilizing the training step. We conducted extensive experiments on three different benchmark datasets, where our proposed model demonstrates its efficiency for item recommendation.