Learning Recommender Systems with Deep Structured Low Rank Matrix Approximation
Mahan Niknafs Kermani · 2020
Collaborative filtering (CF) is a successful approach in developing recommender systems for many real-world problems.Traditional CF-based methods exploit user-item matrix for learning latent factors to make recommendations.Despite their success, data sparsity and cold start problems limit the efficiency of CF-based methods in learning effective latent factors.A classical approach to address these problems is by taking advantage of user (item) auxiliary information.Due to the natural ability of deep learning in extracting features from multiple sources of data simultaneously, it is the most common technique for integrating rating data and auxiliary information.In spite of the success of deep learning in extracting complex and non-linear features from multiple sources of data, sparsity of auxiliary information degrades the quality of the learned latent factors.Another approach that recently gained much attention in improving the performance of CF-based methods assumes the rating matrix is composed of several local regions within each of which users have similar preferences.In this technique, the rating matrix decomposes into several sub-matrices and is approximated locally.This technique however relies on the rating matrix as the sole source of information for learning, and the sparsity of the rating matrix reduces the effectiveness of the learned latent factors.To address the above problems, we propose a Deep structured LOw Rank Matrix Approximation model (DLORMA) that incorporates additional stacked denoising autoencoders and local matrix approximations in a loosely coupled fashion.To the best of our knowledge, DLORMA is the first hybrid recommendation system that combines deep learning and low rank matrix approximation.Experiments conducted on three real datasets show improvements in prediction performance over the component approaches individually.