BiGNN: A Bilateral-Branch Graph Neural Network to Solve Popularity Bias in Recommendation

Abstract

Traditional recommendation methods aim to recom-mend personalized items by analyzing user's history interaction data. They ignore the fact that the data follows a long-tail distribution, which means that a small number of popular items account for most of the interaction records. This phenomenon causes the model to recommend more popular items, resulting in a severe popularity bias. In order to pay more attention to the long-tail items and debias the popular bias, we propose a Bilateral-Branch Graph Neural Network(BiGNN). In the long- tail branch, we construct a separate long-tail sub graph by eliminating the popular items with high degree. When the Graph Neural Network(GNN) aggregates information layer by layer in the subgraph, the receptive field of the single hop becomes larger, which increases the exposure of the long-tail items. Besides, another branch takes the original interaction graph as input to learn the general data distribution and generate the global embeddings of users and items. The two branches use the same GNN structure and share parameters. We employ the point-wise mutual information (PMI) strategy to indicate interaction between users and reconstruct the long-tail sub graph. The two branches are aggregated through an accumulated learning module, which makes the model first learn the conventional patterns and then pay attention to the long-tail data gradually. Extensive experiments on three real-world datasets show that BiGNN evidently outperforms the state-of- the-art methods consistently.