Multi-task feature and structure learning for user-preference based knowledge-aware recommendation

Abstract

Data sparsity and long-tail items recommendation, two typical problems of recommender systems, can be effectively alleviated by leveraging user-side information and item-side information. Knowledge graph (KG) is a source of side information. Due to the specificity of data distribution, the current KG-enhanced recommendation algorithms have the problem of weak generalization and robustness (the performance varies greatly in multiple data sets). Moreover, the previous knowledge-aware recommendation models are insufficient to distill the collaborative signal and personal features from the collective behaviors of users while simultaneously distilling the connectivity and exclusive entity features from the knowledge graph on the item-side. In this paper, we propose a multi-task framework Multi-Rec with high flexibility and adaptability based on alternating learning, which consists of a recommender system (RS) task and a knowledge graph embedding (KGE) task. The RS task consists of user feature learning and user structure learning modules. Correspondingly, the KGE task consists of knowledge graph feature learning and knowledge graph structure learning modules. The correlation between each sub-learning task is carried out by designed cross units and exchange units. Through the end-to-end framework, our model generates high-efficiency node embeddings for downstream recommender tasks and outperforms several state-of-the-art baselines on four real-world datasets through extensive experiments.