Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 189-193.

• Data Science • Previous Articles     Next Articles

Research and Application of Multi-label Learning in Intelligent Recommendation

ZHU Zhi-cheng1, LIU Jia-wei1,2, YAN Shao-hong1,2   

  1. (Hebei Key Laboratory of Data Science and Application,Tangshan,Hebei 063210,China)1;
    (Mathematical Modeling Innovation Laboratory,North China University of Science and Technology,Tangshan,Hebei 063210,China)2
  • Online:2019-11-10 Published:2019-11-20

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Abstract: Collaborative filtering algorithm is used in traditional intelligent recommendation,but it can’t deal with user’srating information well.The data sparsity and extreme data influence the quality of recommendation.Therefore,the recommendation problem is transformed into a multi-label learning problem,and a complete intelligent recommendation system based on HMM model and user portrait was proposed in this paper.Firstly,different data processing mechanisms are set up to improve the generalization ability of the algorithm.Secondly,an improved HMM model with anti-Markov property is proposed to solve the problem of data sparsity.Finally,a user portrait is constructed to screen the learning experience of the HMM model and get the final recommendation service.Experimental results show that multi-label learning can effectively improve the accuracy and efficiency of intelligent recommendation.

Key words: Data sparsity, Intelligent recommendation system, Multi-label learning, User portrait

CLC Number: 

  • TP3-05
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