VIGA: A variational graph autoencoder model to infer user interest representations for recommendation

Learning representations of both user interests and item characteristics is essentially important for recommendation tasks. Although graph neural network-based methods have significant advantages, there are still two inherent limitations: (i) users or items are often modeled as single embedding points in the vector space, and (ii) these methods usually focus more on the graph structure of user-item interactions, rather than the attribute-related information and useful attribute interactions. These limitations make it difficult to model complex user interests and learn high-quality representations, thus fail to obtain high recommendation accuracy. To overcome these limitations, we propose a variational inference-based graph autoencoder (VIGA) model to explore a multivariate distribution over latent representations for recommendation. In brief, to identify both of the complex users' interests and their interactive behaviors, VIGA first encodes users and items into latent variables, and subjects them to a multivariate Gaussian distribution via a graph autoencoder framework based on Bayesian variational inference. Then a feature fusion layer is established for the exchange and integration of both the topological structure and nodes attributes in the user-item interaction graph. Experiments on three real-world datasets show that the proposed VIGA method significantly outperforms state-of-the-art recommendation methods on recommendation accuracy.