Deep latent factor model for collaborative filtering

Aanchal Mongia (1) , Neha Jhamb (1) , Emilie Chouzenoux (2, 3) , Angshul Majumdar (1)

1 IIIT-Delhi - Indraprastha Institute of Information Technology [New Delhi]
2 CVN - Centre de vision numérique
3 OPIS - OPtimisation Imagerie et Santé

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.

Mots clés: Latent semantic analysis; Collaborative filtering; Deep learning; Recommender systems

Domaines: Traitement du signal et de l'image [eess.SP]

Aanchal Mongia, Neha Jhamb, Emilie Chouzenoux, Angshul Majumdar. Deep latent factor model for collaborative filtering. Signal Processing, 2020, 169, pp.107366. ⟨10.1016/j.sigpro.2019.107366⟩. ⟨hal-02373934⟩