KLGCN: Knowledge graph-aware Light Graph Convolutional Network for recommender systems

Most popular recommender systems learn the embedding of users and items through capturing valuable information from user–item interactions or item knowledge graph (KG) with Graph Convolutional Network. However, only a few methods capture information from both source data, and they introduce many trainable parameters that increase training difficulty. In this work, we aim to aggregate information from both the user–item interaction graph and the item KG in a light and effective manner. We first experimentally verify the effectiveness of removing feature transformation and nonlinear activation in KG-aware recommendation, which has been proven to greatly reduce parameters while improving performance in the collaborative filtering-based recommendation. Then we propose a new Knowledge graph-aware Light Graph Convolutional Network (KLGCN), which can learn partial embeddings of users and items by aggregating features on the source graphs for recommendation and introduces no extra parameters. Extensive experiments on three public datasets demonstrate that KLGCN achieves substantial improvement over several state-of-the-art models and maintains satisfactory performance on cold-start scenarios.