| Summary: | Session-based recommendation is a crucial task aiming to predict users’ interested items based only on anonymous user behaviors. Most recent solutions for session-based recommendation comprehensively consider the interactive information of all sessions but bring the problem of imbalanced positive and negative samples on model training. In this paper, we propose a novel approach, named Attention-enhanced Graph Neural Networks with Global Context for Session-based Recommendation (AGNN-GC), to learn and merge item transitions of all sessions in a cleverer way to enhance the recommendation effects. AGNN-GC first constructs global and local graphs based on all training sequences. Next, it uses graph convolutional networks with a session-aware attention mechanism to learn global-level item embedding in all sessions. Then it employs a graph attention networks module to learn local-level item embedding in the current sessions. After that, it fuses the learned two-level item embedding to enhance the feature presentations of items in the current session by a novel attention mechanism. Finally, applying the focal loss to balance positive and negative samples on model training accomplishes the prediction. Our experiments on three real-world datasets consistently show the superior performance of AGNN-GC over state-of-the-art methods.
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