2021/10/08 by Chao Huang, Jiahui Chen, Huang, Chao +15 · 13 citations
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Context (archaeology) #Data mining #Dynamics (music) #Embedding #Encoder #FOS: Computer and information sciences #Graph #Hierarchy #Information Retrieval (cs.IR) #Machine learning #Recommender Systems and Techniques #Relation (database) #Session (web analytics) #Task (project management) #Theoretical computer science #Topic Modeling #Transition (genetics) #World Wide Web #cs.AI #cs.IR
paper · pdf · doi:10.48550/arxiv.2110.03996
published in arXiv (Cornell University) (Cornell University) · Published as a paper at AAAI 2021
arxiv created 2021/10/08 · openalex publication_date 2021/10/08 · arxiv updated 2021/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural network) are not well-designed for capturing the complex transition dynamics exhibited with temporally-ordered and multi-level inter-dependent relation structures. These methods largely overlook the relation hierarchy of item transitional patterns. In this paper, we propose a multi-task learning framework with Multi-level Transition Dynamics (MTD), which enables the jointly learning of intra- and inter-session item transition dynamics in automatic and hierarchical manner. Towards this end, we first develop a position-aware attention mechanism to learn item transitional regularities within individual session. Then, a graph-structured hierarchical relation encoder is proposed to explicitly capture the cross-session item transitions in the form of high-order connectivities by performing embedding propagation with the global graph context. The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space. Extensive experiments on three real-world datasets demonstrate the superiority of MTD as compared to state-of-the-art baselines.