Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 220800205-8.doi: 10.11896/jsjkx.220800205

• Big Data & Data Science • Previous Articles     Next Articles

Global Feature Enhanced for Session-based Recommendation

JIN Bowen1,2, WANG Qingmei1,2, HU Chengzuo1, WEI Jiacheng1   

  1. 1 National Center for Materials Service Safety,University of Science and Technology Beijing,Beijing 100083,China
    2 Innovation Group of Marine Engineering Materials and Corrosion Control,Southern Marine Science and Engineering Guangdong Laboratory,Zhuhai,Guangdong 519080,China
  • Published:2023-11-09
  • About author:JIN Bowen,born in 1999,postgraduate.His main research interests include data mining and recommendation.
    WANG Qingmei,born in 1975,Ph.D,associate researcher.Her main research interests include deep learning and computer vision.
  • Supported by:
    Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(311020012).

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Abstract: Most session-based recommendation system researches commonly model current session user preferences by aggregating K-hop neighborhoods of nodes while using shallow neural networks.However,such methods face the problem of over-smoo-thing.This paper proposes global feature enhanced for session-based recommendation(GFE-SR).Firstly,this method utilizes graph neural network and attention mechanism to obtain session-level item representations.Then,in feature propagation stage of the global graph,the nearest neighbors of each node are proportionally weighted to limit over-smoothing to obtain the feature-enhanced global-level item representations.These two item representations are aggregated through an attention mechanism to model current session user preferences.The final output is the probability of the candidate item.Experiments on three public datasets show that this method outperforms the state-of-the-art methods such as GCE-GNN,with a maximum improvement up to 5.2%,which proves the effectiveness of the proposed method.

Key words: Session-based recommendation, Recommendation system, Graph convolutional network, Attention mechanism, Over-smoothing

CLC Number: 

  • TP391
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