Hybrid-Order Gated Graph Neural Network for Session-Based Recommendation

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 3; pp. 1458 - 1467
• Main Authors: Chen, Yan-Hui, Huang, Ling, Wang, Chang-Dong, Lai, Jian-Huang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Graph neural network; Graph neural networks; Graph theory; hybrid-order; Informatics; Logic gates; Markov processes; Neural networks; oversmoothing; Propagation; Recommender systems; Recurrent neural networks; session-based recommendation; Smoothing methods; Task analysis
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3091435

Considering sessions as directed subgraphs, graph neural networks (GNNs) are supposed to be capable of capturing the complex dependencies among items and suitable for session-based recommendation. However, deep GNNs suffer from the oversmoothing problem of making all nodes converge to the same value. In session-based recommendation, the subgraphs transformed by short sessions are usually simple, which cause worse oversmoothing problem. To apply GNNs to session-based recommendation sufficiently, in this article, we propose a hybrid-order gated GNN (HGNN) on account of the oversmoothing problem. The proposed HGNN model is based on the hybrid-order propagation, which avoids insignificant patterns and captures complex dependencies in propagation. What's more, the attention mechanism is utilized to learn different weights of orders in propagation. Then, HGNN is applied to session-based recommendation, which results in a new method called SR-HGNN. Experimental results show that SR-HGNN outperforms the state-of-the-art session-based recommendation methods and eases the oversmoothing problem.