Dual Quaternion Based Collaborative Knowledge Graph Modeling for Recommendation

To alleviate the issues caused by sparse user-item interaction data and cold-start problems, which limit the recommendation performance of collaborative filtering, researchers integrate the knowledge graph (KG) to recommender systems. However, most existing KG-aware recommendation models use the single transformational method for KG embedding, which may limit the modeling of complicated relations existing in real-world data. Besides, these models often represent users and items using real-valued embeddings, which are of less representation capacity. In this paper, we propose Dual Quaternion-based Collaborative Knowledge Graph Modeling for Recommendation(DQKGR), which represents users and items with dual quaternion embeddings in hypercomplex space, so that the latent inter-dependencies between entities and relations could be captured effectively. In the core of our model, we propose a method called Dual Quaternion-based and Mobius Transformation-based Knowledge Graph Embeddings(DQKGE), which can capture multiple complex relations in collaborative KGs. On top of this, those embeddings are updated by a customized preference propagation and aggregation method with structure information concerned. Finally, we apply the proposed DQKGR to three real-world datasets, including Last-FM, MovieLens-20M, and Book-Crossing. Results show that DQKGR outperforms existing methods on several metrics, and achieves a more than 2.83% performance gain in average. © 2022, Science Press. All right reserved.