KMPR-AEP: Knowledge-Enhanced Multi-task Parallelized Recommendation Algorithm Incorporating Attention-Embedded Propagation

Existing knowledge graph-based recommendation algorithms either use Knowledge Graphs (KGs) as auxiliary information or use KG prediction tasks as regular terms to constrain recommendation with multi-task learning. However, the first method, which introduces multi-hop neighbors to enhance item representations, also weakens the relationship information within individual triples to some extent. The second method ignores neighboring information and thus fails to capture the long-range connectivity between items, which leads to a lack of utilization of the structural information in the KG. In response to the limitations of existing recommendation algorithms that do not fully leverage the relational and structural information within KGs, we propose a novel method named Knowledge-Enhanced Multi-Task Parallelized Recommendation Algorithm Incorporating Attention-Embedded Propagation (KMPR-AEP). It employs a parallel approach to simultaneously incorporate KG as auxiliary information and a regular term. We take into account the multi-hop neighbor information of users and items as well as enhance the relations among individual triples, which considers both the structural and relational information of KG. Extensive experiments conducted on three real-world datasets for CTR prediction and Top-K recommendation scenarios demonstrate the superiority of our proposed method over the state-of-the-art. On average, our KMPR-AEP shows improvements of 2.44% in AUC, 3.49% in ACC, and 2.64% in F1.