[Objective] This study aims to improve the traditional session sequence recommendation algorithm, whose one-time modeling technique cannot represent the product’s comprehensive information or capture the user’s global/short-term interests. [Methods] First, we constructed a directed session graph based on the historical sessions, and used the GNN to learn their node information representation to enrich node embeddings. Then, we captured the user’s global and short-term interests in session sequences with Bi-GRU and attention mechanism to generate recommendation lists. [Results] We examined our new algorithm with the Yoochoose and the Diginetica datasets. Compared with the suboptimal model, the Mean Reciprocal Rank of our algorithm improved by 1.02%, and the precision improved by 2.11%, respectively. [Limitations] The proposed model did not work well with the long sequences. [Conclusions] Our new algorithm can more effectively model the user behavior sequence, predict the user’s possible actions, and improve the recommendation lists.
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