VAE++: Variational AutoEncoder for Heterogeneous One-Class Collaborative Filtering

Neural network-based models for collaborative filtering have received widespread attention, among which variational autoencoder (VAE) has shown unique advantages in the task of item recommendation. However, most existing VAE-based models only focus on one type of user feedback, leading to their performance bottlenecks. To overcome this limitation, we propose a novel VAE-based recommendation model called VAE++, which can effectively utilize heterogeneous feedback to boost recommendation performance. Specifically, it combines three different types of signals, i.e., purchase feedback, examination feedback and their mixed feedback, via two well-designed modules, i.e., a target representation enhancement module and a target representation refinement module. The former exploits the mixed feedback to improve the learning of purchase representations, while the latter leverages the examination feedback to further refine them. In particular, purchase and examination preferences are jointly decoded in one decoder to ensure the high quality of the reconstructed samples. Extensive experiments on three public datasets show that our VAE++ achieves the best results compared with several state-of-the-art methods.