Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Xiangde Zhang, Yuan Zhou, Jianping Wang, Xiaojun Lu
2021 Entropy  
Session-based recommendations aim to predict a user's next click based on the user's current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user's interest preferences at the current moment, thus limiting the accuracy of recommendations.
more » ... ring the diversity of items and users' interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users' interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users' long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.
doi:10.3390/e23111500 pmid:34828197 pmcid:PMC8618736 fatcat:ms7keg7bn5am7e7g4nbxmgindy