Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (1): 126-130.doi: 10.11896／j.issn.1002-137X.2019.01.019

• CCDM2018 • Previous Articles     Next Articles

Hybrid Recommendation Algorithm Based on Deep Learning

ZENG Xu-yu1,2, YANG Yan1,2, WANG Shu-ying1, HE Tai-jun1, CHEN Jian-bo1   

  1. (School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China)1
    (Key Lab of Cloud Computing and Intelligent Technology,Sichuan Province,Chengdu 611756,China)2
  • Received:2018-05-07 Online:2019-01-15 Published:2019-02-25

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Abstract: Recommendation system is playing an increasingly indispensable role in the development of e-commerce,but the sparsity of user’s rating data for the items in the recommendation system is often an important reason for the low recommendation accuracy.At present,the recommendation technology is usually used to process the auxiliary information to alleviate the sparsity of the user evaluation and improve the accuracy of the prediction score.Text data can be used to extract the hidden features of the item through related models.In recent years,the deep learning algorithm has developed rapidly.Therefore,this paper chose a variational autoencoder(VAE),which is a new type of network structure with powerful feature extraction capabilities.This paper proposed a novel context-aware recommendation model integrating the unsupervised method VAE into the variable matrix factorization (VAEMF) in the probability matrix factorization (PMF).Firstly,TD-IDF is used to preprocess the evaluation documents of the item.Then,the VAE is utilized to capture the context information features of the item.Finally,the probability matrix factorization is used to improve the accuracy of the prediction score.The experimental results on two real data sets show that this method is superior to the autoencoder and the probability matrix factorization recommendation methods.

Key words: Deep learning, Matrix factorization, Recommendation system, Variational autoencoder

CLC Number: 

  • TP391.3
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