Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 102-108.doi: 10.11896/jsjkx.230600078

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

Sequential Recommendation Based on Multi-space Attribute Information Fusion

WANG Zihong1, SHAO Yingxia1, HE Jiyuan2, LIU Jinbao2   

  1. 1 School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China
    2 Meituan Platform Search Recommendation Algorithm Department,Beijing 100102,China
  • Received:2023-06-08 Revised:2023-10-15 Online:2024-03-15 Published:2024-03-13
  • About author:WANG Zihong,born in 1998,postgra-duate,is a student member of CCF(No.P2491G).His main research interest is recommender system.SHAO Yingxia,born in 1988,Ph.D,associate professor,is a senior member of CCF(No.17559S).His main research interests include large-scale graph ana-lysis,parallel computing framework,and graph learning.
  • Supported by:
    National Natural Science Foundation of China(62272054,62192784) and Xiaomi Young Talents Program.

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Abstract: The goal of sequential recommendation is to model users' dynamic interests from their historical behaviors,and hence to make recommendations related to the users' interests.Recently,attribute information has been demonstrated to improve the performance of sequential recommendation.Many efforts have been made to improve the performance of sequential recommendation based on attribute information fusion,and have achieved success,but there are still some deficiencies.First,they do not explicitly model user preferences for attribute information or only model one attribute information preference vector,which cannot fully express user preferences.Second,the fusion process of attribute information in existing works does not consider the in-fluence of user personalized information.Aiming at the above-mentioned deficiencies,this paper proposes sequential recommendation based on multi-space attribute information fusion(MAIF-SR),and proposes a multi-space attribute information fusion framework,fuse attribute information sequence in different attri-bute information spaces and model user preferences for different attribute information,fully expressing user preferences using multi-dimensional interests.A personalized attribute attention mechanism is designed to introduce user personalized information during the fusion process,enhance the personalized effect of the fusion information.Experimental results on two public data sets and one industrial private data set show that MAIF-SR is superior to other comparative sequential recommendation models based on attribute information fusion.

Key words: Sequential recommendation, Item attributes, Information fusion, User personalization, Attention mechanism, Multi-dimensional interests

CLC Number: 

  • TP391
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