Ranhao Guo

Learning Multi-interest Embedding with Dynamic Graph Cluster for Sequention Recommendation

Multi-interest recommendation is to predict the next item by representing diversity of a user preference with multiple interest embeddings. Although existing methods have achieved convincing results in recommendation tasks, they ignore the continuously changing relations of no-adjacent items in a se