TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
This work addresses the limitation of fixed session representations in recommender systems, which is an incremental improvement for enhancing user experience on websites.
The paper tackles the problem of session-based recommendation by proposing TAGNN, a model that adapts session representations to target items using target-aware attention, achieving state-of-the-art performance on real-world datasets.
Session-based recommendation nowadays plays a vital role in many websites, which aims to predict users' actions based on anonymous sessions. There have emerged many studies that model a session as a sequence or a graph via investigating temporal transitions of items in a session. However, these methods compress a session into one fixed representation vector without considering the target items to be predicted. The fixed vector will restrict the representation ability of the recommender model, considering the diversity of target items and users' interests. In this paper, we propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model. Moreover, TAGNN harnesses the power of graph neural networks to capture rich item transitions in sessions. Comprehensive experiments conducted on real-world datasets demonstrate its superiority over state-of-the-art methods.