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| Session-Based Recommendation Model with Self Contrastive Graph Neural Network and Dual Predictor |
| ZHANG Yusong1, XIA Hongbin1,2, LIU Yuan1,2 |
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122;
2. Jiangsu Key University Laboratory of Software and Media Te-chnology under Human-Computer Cooperation, Jiangnan University, Wuxi 214122 |
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Abstract Session-based recommendation aims to predict user behavior based on short-term anonymous sessions. In most of the existing session-based recommendation models using graph neural network and contrastive learning, joint optimization of cross-entropy loss and contrastive learning loss is typically adopted. However, these two methods play similar roles and require the construction of a large number of complex positive and negative samples, bringing a burden to the model. Moreover, simple linear predictor struggles to predict the data with random behaviors of users. To solve the problems, a session-based recommendation model with self contrastive graph neural network and dual predictor is proposed(SCGNN). Firstly, the original session is built into two views, an improved graph neural network is employed to learn item and session embeddings, and item representation is optimized by self-contrastive learning. Then, a user behavior-aware factor is introduced to mitigate the impact of user random behaviors. Finally, the decision forest predictor and linear predictor are both utilized to predict the items, and soft label generation strategy is proposed for assist prediction by collaboratively filtering the historical sessions similar to the current session. Experiments on three benchmark datasets, Tmall, Diginetica and Nowplaying, validate the effectiveness of SCGNN.
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Received: 26 January 2024
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| Fund:National Natural Science Foundation of China(No.61972182) |
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Corresponding Authors:
XIA Hongbin, Ph.D., professor. His research interests include personalized recommendation and natural language processing.
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| About author:: ZHANG Yusong, Master student. His research interests include recommendation systems and deep learning.LIU Yuan, Ph.D., professor. His research interests include network security and social network. |
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2024, Vol. 37 
