Abstract:Session-based recommendation (SBR) emphasizes mining user interests to predict the next click based on recent interactions within sessions. Most current SBR methods suffer from insufficient interactive information problems and fail to distinguish session representations with high similarities, which can neglect the inherent features within sessions. To fill the gap, we propose a triplet mining enhanced graph neural networks (TME-GNN) approach to enhance the recommendation systems by mining structural and inher… Show more
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