Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231100047-9.doi: 10.11896/jsjkx.231100047

• Big Data & Data Science • Previous Articles     Next Articles

MB-ATMK:Multi-behavior Sequential Recommendation Integrating Attribute Weights andTemporal Meta-knowledge

CHEN Yuzhe, CAO Qiong, HUANG Xianying, ZOU Shihao   

  1. College of Computer Science and Engineering,Chongqing University of Technology,Chongqing 400054,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:CHEN Yuzhe,born in 1999,master.His main research interetes include recommendation system and so on.
    CAO Qiong,born in 1979,master,lecturer.Her main research interetes include data science and deep learning.
  • Supported by:
    National Natural Science Foundation of China(62141201) and Natural Science Foundation of Chongqing,China(CSTB2022NSCQ-MSX1672).

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Abstract: Sequential recommendation predicts users' future preferences based on the sequence of interactions between users and items.However,existing methods often overlook the multi-behavior interactions(such as page view,favorite,add to cart)in real-world scenarios.Additionally,users' preferences not only depend on temporal sequences but are also influenced by attribute information.Lastly,in the scenario of multi-behavior sequence recommendation,users' multi-behavior interactions exhibit complex dependencies.Therefore,this paper proposes a multi-behavior sequence recommendation model with attribute weights and temporal meta-knowledge(MB-ATMK).Firstly,we incorporate users' multi-behavior interaction data and design a temporal-aware encoding module based on the timestamps of user interactions to capture users' dynamic preferences through temporal-aware attention.Secondly,we introduce rich attribute information on both the user and item sides and design an attribute-weighted meta-knowledge graph neural network.Using meta-knowledge,we refine users' multi-preference patterns and design an attribute-weighted attention mechanism based on graph neural networks to enhance the model's capture of users' fine-grained preferences.Finally,we propose a meta-knowledge prediction layer that includes a multi-behavior weight generation module and a preference transfer network,capturing users' cross-behavior dependencies through generated customized meta-knowledge.Extensive experiments on two datasets validate the effectiveness and superiority of the proposed model.

Key words: Sequential recommendation, Multi-behavior recommendation, Graph neural network, Attention mechanism, Attribute information

CLC Number: 

  • TP391
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