Knowledge Graph-Based Personalized Multitask Enhanced Recommendation

To address the problem of data sparsity in recommendation systems, various studies have used knowledge graphs as auxiliary information. These studies have employed multitask learning (MTL) to enhance recommendation performance. However, the shared information between tasks is not fully explored when using an MTL strategy for training both recommendation and knowledge graph-related tasks. Moreover, most studies cannot effectively model the knowledge sharing, consequently affecting recommendation performance. In response to these problems, we proposed a novel knowledge graph-based personalized multitask enhanced recommendation model. To explore the shared information between tasks, a relation attention mechanism was proposed to distinguish the relative importance of neighborhood information to the central entity. Additionally, we utilized a lightweight graph convolutional network to more effectively aggregate high-order neighborhood information from the knowledge graph. This approach improves the accuracy of neighborhood feature and ensures that more suitable shared information is obtained. Furthermore, we developed a linear interaction component to model knowledge sharing between recommendation and knowledge graph embedding tasks. This component allows for detailed feature interaction learning between items and entities, enhancing the shared feature representation, generalization capabilities, and overall performance of the recommendation system. The experimental results on three public datasets indicate that our model outperforms other benchmark models in CTR prediction and top-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{K}$</tex-math></inline-formula> recommendation.