%0 Journal Article %A WANG Yonggui %A WANG Yang %A TAO Mingyang %A CAI Yongwang %T Global Information Attention Enhancement for Session-Based Recommendation %D 2022 %R 10.3778/j.issn.1002-8331.2201-0116 %J Computer Engineering and Applications %P 131-141 %V 58 %N 21 %X In recent years, the application and research of session-based recommender system(SRS) is a popular direction of recommender system. How to use user session information to further improve user satisfaction and recommendation accuracy is the main task of session-based recommendation system. At present, most SBR models only model user preferences based on the target session, ignoring the item conversion information from other sessions, thus failing to fully understand user preferences. To address its limitations, this paper proposes a global context information graph neural networks for session-based recommendation(GCI-GNN). This model takes advantage of the item conversion relationship across all sessions to capture user preferences more accurately. Specifically, GCI-GNN first learns object vector representations from both the target session and the global session. Secondly, location awareness attention network is used to incorporate reverse location information into object embedding. Finally, more effective recommendation can be achieved by learning user representation based on session length information. In this paper, experiments are carried out on Diginetica and Yoochoose data sets. The experimental results show that, compared with the optimal benchmark model, the GCI-GNN model improves the indexes of Diginetica data set by more than 2 percentage points. On Yoochoose data set, the GCI-GNN model improves more than 1 percentage point in each index, which verifies the effectiveness of the GCI-GNN model. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2201-0116