在电商场景中,用户面对繁杂的商品时往往难以快速检索到所需商品,而基于会话的商品推荐能通过学习用户短期兴趣从而为其推荐可能感兴趣的商品,因此基于会话的推荐研究具有显著的理论和应用研究价值。已有的会话推荐算法大多关注于利用全局图中的信息来增强会话图中的表征学习,而忽略了会话图和全局图上物品表征之间的交互关系。该文提出一种通过交互注意力和改进参数自适应策略增强的图神经网络商品会话推荐模型。交互注意层通过提取强相关信息来修正全局图和会话图中的商品表示,而参数自适应层则通过改进参数自适应策略动态权重调整以获得物品的最终表示进而用于预测。实验结果表明,该文所提出的模型在Tmall数据集上显著优于对比模型。
Abstract
In the E-Commence scenario, it is a crucial issue to construct an effective Session-Based Recommendation (SBR) model, helping users quickly find products of interest based on their short-term interests. Most current models fails to capture the interactive relationship of item embeddings on the session graph and global graph. This paper proposes to utilize the interactive attention and improved parameter self-adaptive strategy to enhance the GNN-based commodity recommendation model. The interactive attention layer is applied to amend the commodity representation in the global graph and the session graph through strong correlation extraction, and the parameter adaptive layer is to obtain the final representation of the items for prediction. Experimental results show that our model is significantly superior to other off-the-shelf models on the public dataset of Tmall.
关键词
会话推荐 /
图神经网络 /
交互注意力机制 /
改进参数自适应
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Key words
session-based recommendation /
graph neural network /
interactive attention mechanism /
improved parameter self-adaption
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参考文献
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脚注
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基金
国家重点研发计划(2018YFC0807105);上海市科学技术委员会科研计划项目(22DZ1204903,22511104800)
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