Temporal heterogeneous interaction graph embedding for next-item recommendation

• Main Authors: JI, Yugang, YIN, Mingyang, FANG, Yuan, YANG, Hongxia, WANG, Xiangwei, JIA, Tianrui, SHI, Chuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Temporal heterogeneous interaction graph; Next-item recommendation; Short-term demands; Long-term habit; Computer Engineering; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5157; https://ink.library.smu.edu.sg/context/sis_research/article/6160/viewcontent/ECML20_THIGE.pdf
• Institution: Singapore Management University

In the scenario of next-item recommendation, previous methods attempt to model user preferences by capturing the evolution of sequential interactions. However, their sequential expression is often limited, without modeling complex dynamics that short-term demands can often be influenced by long-term habits. Moreover, few of them take into account the heterogeneous types of interaction between users and items. In this paper, we model such complex data as a Temporal Heterogeneous Interaction Graph (THIG) and learn both user and item embeddings on THIGs to address next-item recommendation. The main challenges involve two aspects: the complex dynamics and rich heterogeneity of interactions. We propose THIG Embedding (THIGE) which models the complex dynamics so that evolving short-term demands are guided by long-term historical habits, and leverages the rich heterogeneity to express the latent relevance of different-typed preferences. Extensive experiments on real-world datasets demonstrate that THIGE consistently outperforms the state-of-the-art methods.