Back to Search
Start Over
Integrating Stacked Sparse Auto-Encoder Into Matrix Factorization for Rating Prediction
- Source :
- IEEE Access, Vol 9, Pp 17641-17648 (2021)
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Currently, collaborative filtering technology has been widely used in personalized recommender systems. The problem of data sparsity is a severe challenge faced by traditional collaborative filtering methods based on matrix factorization techniques. A lot of improved collaborative filtering methods have been proposed to alleviate the data sparsity problem; However, due to the sparsity of the user rating matrix, the latent factor learned by these improved methods may be not efficient. In this paper, we propose a novel recommendation algorithm named SSAERec by integrating stacked sparse auto-encoder into matrix factorization for rating prediction, which can learn effective representation from user-item rating matrix. Extensive experiments on three real-world datasets demonstrate the proposed method outperforms other baselines in the rating prediction task.
- Subjects :
- General Computer Science
Computer science
Feature extraction
02 engineering and technology
Recommender system
computer.software_genre
Matrix decomposition
Data modeling
data sparsity
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Collaborative filtering
rating prediction
General Materials Science
Representation (mathematics)
Sparse matrix
business.industry
Deep learning
General Engineering
Autoencoder
collaborative filtering
Sparse auto-encoder
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
Data mining
Artificial intelligence
business
lcsh:TK1-9971
computer
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....49a046552112a46b83d9ce87d250ad45