V-GMR: a variational autoencoder-based heterogeneous graph multi-behavior recommendation model

Compared to traditional single-behavior models, multibehavior recommendation models incorporate the auxiliary behavior information of users. This integration step addresses the cold-start and data sparsity issues and provides more comprehensive and detailed interaction information for the model. Despite the efforts made by multibehavior recommendation models to analyze user behavior semantics and capture user preferences, challenges remain in terms of effectively modeling the relationships between different types of user feedback. This issue is exacerbated by the heavy reliance on hyperparameters, which leads to overparameterization. In this paper, we propose a variational autoencoder (VAE) and graph-based heterogeneous multibehavior recommendation model (V-GMR), which aims to capture user behavior preferences and mitigate the aforementioned issues. First, we employ VAEs to encode user behaviors and learn feature representations that effectively capture multibehavior information. Second, we develop a preference fusion enhancer based on a VAE to integrate auxiliary user behaviors with the target behavior, effectively addressing the problem concerning sparse interaction data. Third, we design a special behavior decoding layer to handle the latent variables acquired from the preference fusion enhancer. In this layer, we reconstruct the loss function and resolve the issue of optimizing the neural network parameters through backpropagation in the presence of deterministic input values. The effectiveness of V-GMR is validated through experiments conducted on three real-world datasets, and the contributions of the V-GMR model components are verified through ablation experiments.