融合交互注意力和参数自适应的商品会话推荐

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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.

Key words： session-based recommendation graph neural network interactive attention mechanism improved parameter self-adaption

Received: 09 March 2021

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	Articles by authors
	ZHENG Nan
	GUO Yi
	LI Zhiqiang
	WANG Zhihong

Cite this article:

ZHENG Nan,GUO Yi,LI Zhiqiang, et al. Session-Based Commodity Recommendation Through Interactive Attention and Parameter Self-Adaption[J]. , 2022, 36(11): 131-139.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I11/131

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