Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (9): 41-47.doi: 10.11896/jsjkx.220200131

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

Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer

ZHANG Jia, DONG Shou-bin   

  1. School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China
    Zhongshan Institute of Modern Industrial Technology of SCUT,Zhongshan,Guangdong 528437,China
  • Received:2022-02-22 Revised:2022-06-08 Online:2022-09-15 Published:2022-09-09
  • About author:ZHANG Jia,born in 1996,postgra-duate.Her main research interests include recommendation systems and natural language processing.
    DONG Shou-bin,born in 1967,Ph.D,professor,Ph.D supervisor.Her main research interests include information retrieval,natural language processing and high-performance computing.

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Abstract: In order to solve the user cold-start problem caused by data-sparse in recommender system,this paper proposes a cross-domain recommendation algorithm based on aspect-level user preference transfer,named CAUT.CAUT is devised to learn aspect transfer across domains from a two-stage generative adversarial network and extract aspect-level user fine-grained prefe-rence from reviews.The data distribution misalignment between source and target domains is eliminated by fixing source domain encoder parameters and designing a domain discriminator.Then the user cold-start problem caused by data-sparse in the target domain could be alleviated by utilizing source domain data via CAUT.Experiments on real-world datasets show that the proposed CAUT outperforms SOTA models significantly in rating prediction RMSE indicator,suggesting that CAUT can effectively solve the user cold-start problem.

Key words: Cross-domain recommendation, Aspect-level user preference, Cold-start user, Generative adversarial network

CLC Number: 

  • TP391
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