Diversity-Enhanced Recommendation with Knowledge-Aware Devoted and Diverse Interest Learning

Knowledge graph (KG) is receiving increasing attention from researchers in recommender systems with the help of graph neural networks (GNN). Beyond accuracy, diversity of recommendation is also recognized as a key factor in broadening users' horizons and boosting satisfaction for users. Considering diversity adapted to user demands can facilitate the recommendation performance for both accuracy and diversity. However, most approaches fail to (1) explore the help of KG for diversity, and (2) identify different kinds of latent interests from users for diversity. This paper proposes a diversity-enhanced recommendation with knowledge-aware interests. The user interest consists of devoted interest and diverse interest in our approach modeled by a dual-branch GNN-based learning structure with an adaptive trade-off. The devoted interest learning branch exploits the entity relations from KG to explore the potential patterns of users to improve accuracy, while the diverse interest learning branch additionally combines the category of the items with KG to achieve diversity. Attentive relational aggregation is designed to aggregate the information from KG and user-item interaction for the representations of users and items modeling. Extensive experiments on three real-world datasets show that our approach effectively improves the recommendation accuracy while obtaining impressive diversity. This work is available at https://github.com/ljf012/DERK.