%0 Journal Article %A ZHENG Nan %A GUO Yi %A LI Zhiqiang %A WANG Zhihong %T Session-Based Commodity Recommendation Through Interactive Attention and Parameter Self-Adaption %D 2023 %R %J Journal of Chinese Information Processing %P 131-139 %V 36 %N 11 %X 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. %U http://jcip.cipsc.org.cn/EN/abstract/article_3431.shtml