Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 117-125.doi: 10.11896/jsjkx.210900061

• Database & Big Data & Data Science • Previous Articles     Next Articles

Modeling User Micro-Behavior via Adaptive Multi-Attention Network for Session-based Recommendation

QIAO Jing-jing1, WANG Li2   

  1. 1 College of Information and Computer,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
    2 College of Data Science,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
  • Received:2021-09-07 Revised:2021-10-15 Online:2022-11-15 Published:2022-11-03
  • About author:QIAO Jing-jing,born in 1993,Ph.D candidate,is a student member of China Computer Federation.Her main research interests include recommendation system and data mining.
    WANG Li,born in 1971,Ph.D,professor,is a senior member of China Computer Federation.Her main research interests include big data computation and analysis,knowledge graph and data mining.
  • Supported by:
    National Natural Science Foundation of China(61872260).

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Abstract: Session-based recommendation (SR) aims to recommend the next item that matches the user’s preferences based on their short-term session information.It does not need user’s profile and long-term historical information and has a broad application prospect.Existing SR models usually focus on user click behavior or only use a single type of behavior data,ignoring the specific semantics of user click behavior,such as browsing,collecting,carting,purchasing,and so on.These behaviors with different semantics are called micro-behavior,which reflects the transformation of user intention and decision-making process in the shopping process from the micro level and will provide valuable information for improving the recommendation effect.Therefore, an adaptive multi-attention network (AMAN) based on user micro-behavior is proposed for session-based recommendation.First,we model the sequence of micro-behavior as heterogeneous directed graph,and then build three components for session-based recommendation.Directed graph attention network (DGAT) learns item representation from the item level and adaptively captures the associations between items with the same micro-operation.Operation-level heterogeneous graph attention network (OHGAT) learns item representation at the operation-level and adaptively captures the associations between items with different micro-ope-ration.Micro-behavior co-attention network (MBCAT) learns the representation of micro-behavior sequences and adaptively captures the dependencies between different micro-behavior sequences.Experimental results on Yoochoose,Taobao14 and Taobao15 datasets show that AMAN outperforms the state-of-the-art baselines.

Key words: Session-based recommendation, Micro-behavior, Heterogeneous directed graph, Attention network

CLC Number: 

  • TP391
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