Graph attention networks with adaptive neighbor graph aggregation for cold-start recommendation

The cold-start problem is a long-standing problem in recommender systems, i.e., lack of historical interaction information hinders effective recommendations for new users and items. Existing methods typically incorporate attribute information of users and items to address the strict cold-start problem. Most existing recommendation methods overlook the sparsity of user attributes in cold start recommendation systems. In this paper, we develop a novel framework, Graph Attention Networks with Adaptive Neighbor Graph Aggregation for cold-start Recommendation (A-GAR), which utilizes the user/item relationship information in cold-start recommendation systems to alleviate the sparsity of attributes. we can achieve more accurate recommendations in cold-start scenarios by fully exploring the complex relations between users/items using graph structures. Specifically, to learn the complex relationships between user/item attributes, we utilize SENet (Squeeze and Excitation Network) and MLP (Multilayer Perceptron) networks to adaptively fuse the embeddings of user/item and their second-order interaction vectors, achieving high-order feature aggregation. To address the issue of lacking preference information in cold-start recommendations, we extend the variational autoencoder to reconstruct missing user preferences (item characteristics) from higher-order attribute features of users/items. In order to learn the potential semantic relationships of nodes in the neighbor graph structure, an attribute graph attention network is used to aggregate the neighbor information of users and the interaction information between neighbors. In this way, the high-order relationships between nodes and the potential semantics of adjacent graphs can be fully explored. Extensive experiments on three real-word datasets with various cold-start scenarios demonstrate that A-GAR yields significant improvements for strict cold-start recommendations.