Learning Latent Preference Distributions for VAE Recommenders

Variational autoencoder (VAE)-based collaborative filtering (CF) has emerged as a powerful framework for recommendation, efficiently inferring users' latent representations from user-item interactions without maintaining user-specific parameters. However, existing methods typically adopt the standard Gaussian prior to learn latent user representations, limiting their ability to model multifaceted user preferences. Moreover, existing VAE-based CF methods overlook potential collaborations among users, leaving sparse users with limited supervision signals and further limiting the quality of their latent representations. To address these limitations, we first theoretically analyze how Kullback-Leibler (KL) divergence regularization and reconstruction loss in vanilla VAE-based CFs constrain users' posterior distributions, limiting the overall performance of the methods. Building on these theoretical analyses, we propose LPD-VAE, a VAE-based recommendation framework designed to learn Latent Preference Distributions (LPD) and explore latent preference collaborations among users. First, we initialize a set of preference components based on the Gaussian Mixture Model prior and devise mixture-prior KL regularization to force user posterior distribution to approach these components, thereby learning each user's LPD to characterize rich user preferences. Then, based on the learned user LPDs, we reckon latent preference collaborations among users to discover potential positive samples from uninteracted items, alleviating the sparse interaction issue of VAE-based CFs. Finally, we design a preference-alignment loss supplementing to the original ELBO to encourage user latent representations to align with potential positive samples in the latent space, so as to refine user LPDs. Extensive experiments on three benchmark datasets demonstrate consistent gains over competitive SOTAs. Furthermore, experiments across users with varying interaction densities show that LPD-VAE maintains consistent performance gains under different levels of sparsity. The ablation experiment demonstrated that, compared to the interaction distance in the interaction space, the latent preference distance in the latent space is more effective in learning user preferences.