Interest Evolution-driven Gated Neighborhood aggregation representation for dynamic recommendation in e-commerce |
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| Institution: | 1. Department of Information Resources Management, Business School, Nankai University, Tianjin, China;2. Center for Network Society Governance, Nankai University, Tianjin, China;1. School of Management and Economics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731 Chengdu, Sichuan, PR China;2. The Walker School of Business and Technology, Webster University, 470 E Lockwood Ave, Webster Groves, MO 63119, United States;3. The James F. Dicke College of Business Administration, Ohio Northern University, 525 S Main St, Ada, OH, United States;1. College of Information and Electrical Engineering, China Agricultural University, Beijing 10081, China;2. Scientific Research Base for Integrated Technologies of Precision Agriculture, Ministry of Agriculture, Beijing 10081, China |
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| Abstract: | Recommender system as an effective method to reduce information overload has been widely used in the e-commerce field. Existing studies mainly capture semantic features by considering user-item interactions or behavioral history records, which ignores the sparsity of interactions and the drift of user preferences. To cope with these challenges, we introduce the recently popular Graph Neural Networks (GNN) and propose an Interest Evolution-driven Gated Neighborhood (IEGN) aggregation representation model which can capture accurate user representation and track the evolution of user interests. Specifically, in IEGN, we explicitly model the relational information between neighbor nodes by introducing the gated adaptive propagation mechanism. Then, a personalized time interval function is designed to track the evolution of user interests. In addition, a high-order convolutional pooling operation is used to capture the correlation among the short-term interaction sequence. The user preferences are predicted by the fusion of user dynamic preferences and short-term interaction features. Extensive experiments on Amazon and Alibaba datasets show that IEGN outperforms several state-of-the-art methods in recommendation tasks. |
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| Keywords: | Recommender system User preference Short-term sequence Graph neural network Gated network Aggregation representation |
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