Recurrent Neural Network and Attention Enhanced Gated Graph Neural Network for Session-Based Recommendation
LI Weiyue1,2, ZHU Zhiguo1,2, DONG Hao1,2, JIANG Pan1,2, GAO Ming1,2
1. School of Management Science and Engineering, Dongbei Uni-versity of Finance and Economics, Dalian 116025; 2. Key Laboratory of Liaoning Province for Data Analytics and Decision-Marking Optimization, Dongbei University of Finance and Economics, Dalian 116025
Abstract：Most of existing session-based recommender systems with graph neural networks are capable of capturing the adjacent contextual relation of products effectively in the session graph. However, few of them focus on the sequential relation. Both relations are important for precise recommendations in e-commerce scenarios. To solve the problem, a recurrent neural network and attention enhanced gated graph neural network for session-based recommender system is proposed based on bidirectional long short-term memory. The model is designed to complement the advantages of different network structures and learn the user's interest preferences expressed during the current session more fully. Specifically, a parallel framework is adopted in the proposed model to learn the neighborhood contextual features and temporal relation among products respectively within user session clickstreams in e-commerce scenarios. Attention mechanisms are applied to denoise the features. Finally, the adaptive fusion method of both features is employed based on gating mechanism. Experiments on three real-world datasets show the superiority of the proposed model. The model code in the paper is available at https://github.com/usernameAI/RAGGNN.
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2024, Vol. 37 
