Improving Graph Neural Network For Session-based Recommendation System Via Time Sessions

Session-based recommendations (SBR) have attracted much attention because of their high commercial value. SBR aims to predict a user's next click based on the sequence of previous sessions. However, existing graph neural network (GNN)-based recommendation methods only focus on session graphs and ignore the time frame in the session which contains valuable temporal information for reducing the impact of user's unintentional clicks. Meanwhile, the expression ability of hidden factors in GNNs is neglected. In order to solve the above limitations, this paper propose an improving Graph Neural Network via Time sessions (TGNN) model. For the data preprocessing aspect, TGNN builds temporal sessions from the time frame in the dataset. Dwell time recurrent network explores complex temporal influence on items by learning time features. Multi-head attention is introduced to enhance the expression ability of hidden factors. Target-aware attention is utilized to activate different user interests for different target items adaptively, which can obtain interest expression changes of target items. Comparative experiments are conducted on two public datasets, and experimental results show that our method outperforms state-of-the-art methods. Ablation experiments demonstrate the effectiveness of the proposed model.