Leveraging Hyperbolic Dynamic Neural Networks for Knowledge-Aware Recommendation

Knowledge graph (KG) is of growing significance in enabling explainable recommendations. Recent research works involve constructing propagation-based recommendation models. Nevertheless, most of the current propagation-based recommendation methods cannot explicitly handle the diverse relations of items, resulting in the inability to model the underlying hierarchies and diverse relations, and it is difficult to capture the high-order collaborative information of items to learn premium representation. To address these issues, we leverage hyperbolic dynamic neural networks for knowledge-aware recommendation (KHDNN). Technically speaking, we embed users and items (forming user–item bipartite graphs), along with entities and relations (constituting KGs), into hyperbolic space, followed by encoding these embeddings using an encoder. The encoded embedding is passed through a hyperbolic dynamic filter to explicitly handle relations and model different relational structures. Furthermore, we design a fresh aggregation strategy based on relations to propagate and capture higher-order collaborative signals as well as knowledge associations. Meanwhile, we extract semantic information via a bilateral memory network to fuse item collaborative signals and knowledge associations. Empirical results from four datasets show that KHDNN surpasses cutting-edge baseline methods. Additionally, we demonstrate that the KHDNN can perform knowledge-aware recommendations with complex relations.