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
摘要 基于会话的推荐旨在利用短时匿名会话预测用户行为.现有结合图神经网络与对比学习的会话推荐模型大多采用联合优化交叉熵损失与对比学习损失的方法,但二者所起作用相似,同时需要构建大量复杂的正负样本,为模型带来负担.此外,简单的线性预测器不能较好地预测带有用户随机行为的数据.针对上述问题,文中提出结合自对比图神经网络与双预测器的会话推荐模型(Session-Based Recommendation Model with Self Contrastive Graph Neural Network and Dual Predictor, SCGNN).首先,使用双视图建模原始会话,采用改进的图神经网络学习物品嵌入与会话嵌入,并通过自对比学习优化物品表示.然后,提出用户行为感知因子,应对用户随机行为带来的影响.最后,采用决策森林预测器与线性预测器对物品进行预测,并提出软标签生成策略,通过协同过滤与当前会话类似的历史会话以辅助预测.在Tmall、Diginetica、Nowplaying数据集上的实验表明文中模型的有效性.
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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