SDDRS: Stacked Discriminative Denoising Auto-Encoder based Recommender System

Abstract

Recommender systems are widely used in our life for automatically recommending items relevant to our preference. Collaborative Filtering (CF) is one of the most successful methods in recommendation field. Matrix Factorization (MF) based recommender system is designed according to the basic strategy of the CF algorithm, which is widely adopted recently. However, the rating matrix utilized by these models is usually sparse, so it is of vital significance to integrate the side information to provide relatively effective knowledge for modeling the user or item features. The key problem is to extract effective features from the noisy side information. However, the side information contains a lot of noise except rating knowledge, which makes it a challenging issue for extracting effective features. In this paper, we propose Stacked Discriminative Denoising Auto-Encoder based Recommender System (SDDRS) by integrating deep learning model with MF based recommender system to effectively incorporate side information with rating information. Extensive top-N recommendation experiments conducted on three real-world datasets empirically demonstrate that SDDRS outperforms several state-of-the-art methods.