Frontiers of Data and Computing ›› 2021, Vol. 3 ›› Issue (3): 148-155.

doi: 10.11871/jfdc.issn.2096-742X.2021.03.013

• Technology and Applicaton • Previous Articles    

Hybrid Recommendation Model Based on Autoencoder and Attribute Information

CHEN Zijian1,2(),LI Jun1,*(),YUE Zhaojuan1(),ZHAO Zefang1,2()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2021-02-02 Online:2021-06-20 Published:2021-07-09
  • Contact: LI Jun;;;


[Objective] The traditional collaborative filtering recommendation models cannot extract the complex interactive relationship between users and projects, which causes a certain adverse impact on the final recommendation results. [Methods] To solve this problem, a hybrid recommendation model DAAI (Denoising Autoencoder with Attribute Information) is proposed in this paper. The denoising autoencoder is used to extract the deep nonlinear features of the scoring matrix. On this basis, DNN, CNN, and other methods are used to extract the hidden features in the attribute information. Finally, a multi-layer perceptron is adopted to generate the final prediction score by aggregating various features.[Conclusions] The proposed model is tested on the MovieLens dataset and compared with the traditional recommendation algorithms such as SVD, PMF, and AutoRec. The experiment results show that the DAAI model can achieve better recommendation results. [Limitations] Due to the complex neural network structure, the training cost of our model increases slightly compared with traditional models.

Key words: autoencoder, convolutional neural network, deep learning, recommendation model