Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 439-444.

• Big Data & Data Mining • Previous Articles     Next Articles

Local Model Weighted Ensemble for Top-N Movie Recommendation

TANG Ying1, SUN Kang-gao1, QIN Xu-jia1, ZHOU Jian-mei2   

  1. School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China1
    School of Computer Science and Technology,Nantong University,Nantong,Jiangsu 226019,China2
  • Online:2019-02-26 Published:2019-02-26

Abstract: In order to solve the problem that the traditional recommendation algorithms can not accurately capture the user preference with a single model,this paper proposed a Top-N personalized recommendation algorithm based on local model weighted ensemble.This recommendation algorithm adopts user clustering to compute the local models and takes the sparse linear model as the basic recommendation model.Meanwhile,the semantic-level feature vector representation of each user was proposed based on LDA topic model and movie text content information,so as to implement user clustering.The experiments of the film data crawled from Douban show that our local model weighted ensemble recommendation algorithm enhances the recommendation quality of the original base model and outperforms some traditional classical recommendation algorithms,which demonstrates the effectiveness of the proposed algorithm.

Key words: Recommendation system, Model ensemble, Sparse linear model, Topic model

CLC Number: 

  • TP301
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