[Objective] This study aims to improve the performance of session-based recommendation models in repeat consumption scenarios and reduce the negative impact of information overload. [Methods] First, we improved the Repeat-Explore Mechanism suitable for repeat consumption scenarios. Then, based on Self-Attention Mechanism, we fused the position information in a non-invasive approach to optimize the utilization of side information. The performance of the new model was validated on public datasets. [Results] Compared to the suboptimal values, the Recall and Mean Reciprocal Rank of the new model on the Yoochoose 1/64 dataset increased by 0.71% and 1.69%, respectively. On the Diginetica dataset, the Recall and Mean Reciprocal Rank were improved by 3.08% and 5.72%. [Limitations] Our experiment only used position information as side information, and the datasets used for verification were limited. [Conclusions] The experimental results verify the effectiveness of the proposed model, which could optimize recommendation systems and improve personalized information services.
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