Deep Latent Factor Model for Collaborative Filtering
This work addresses the need for more accurate recommendations in systems like e-commerce or streaming services, but it appears incremental as it builds on existing latent factor models with deep learning.
The authors tackled the problem of improving collaborative filtering in recommender systems by proposing a deeper version of the latent factor model, and experiments on benchmark datasets showed that their technique significantly outperforms all state-of-the-art methods.
Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all state-of-the-art collaborative filtering techniques.