Integrating Stacked Sparse Auto-Encoder Into Matrix Factorization for Rating Prediction

• Main Authors: Yihao Zhang, Chu Zhao, Mian Chen, Meng Yuan
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Sparse auto-encoder; collaborative filtering; data sparsity; rating prediction
• Online Access: https://ieeexplore.ieee.org/document/9335550/

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.