Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 153-158.

• Data Science • Previous Articles     Next Articles

Application of Active Learning in Recommendation System

ZHAO Hai-yan1, WANG Jing1, CHEN Qing-kui1, CAO Jian2   

  1. (School of Optical-Electrical Information & Computer Engineering,University of Shanghai for Science & Technology,Shanghai 200093,China)1;
    (Department of Computer Science & Technology,Shanghai Jiao Tong University,Shanghai 200030,China)2
  • Online:2019-11-10 Published:2019-11-20

Abstract: In recent years,recommender system develops very quickly and is becoming more and more mature.However,many approaches are based on an ideal assumption,i.e.,there are plenty of sample data which can help us train a mature model to predict or recommend.In actual industrial production,most users and products lack of rating information or consumption records.And datasets formed by historical accumulation are unevenly distribued,so that it is hard to learn a reliable model.Active learning considers that the benefits of each item to the system is different,so some special items can be selected through specific strategies,and the related preference information can be actively obtained by the interaction between the user and the project.Active learning applied in the recommendation system attempts to training a model with fewer but higher quality samples,which improves the user experience and protects against unbalanced data sets.The applications of active learning in recommendation system in recent years were reviewed and summarized.Future directions were also discussed in this paper.

Key words: Active learning, Cold start, Conversational-based, Query-by-committ, Recommendation system

CLC Number: 

  • TP311
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