Latent feature modelling for recommender systems
A Alhejaili, S Fatima - … on Information Reuse and Integration for …, 2020 - ieeexplore.ieee.org
Matrix factorization is one of the most successful model-based collaborative filtering
approaches in recommender systems. Nevertheless, useful latent user features can lead to …
approaches in recommender systems. Nevertheless, useful latent user features can lead to …
Latent Feature Modelling for Recommender Systems
A Alhejaili, S Fatima - 2020 IEEE 21st International Conference on …, 2020 - computer.org
Matrix factorization is one of the most successful model-based collaborative filtering
approaches in recommender systems. Nevertheless, useful latent user features can lead to …
approaches in recommender systems. Nevertheless, useful latent user features can lead to …
Latent feature modelling for recommender systems
A Alhejaili, S Fatima - repository.lboro.ac.uk
Matrix factorization is one of the most successful model-based collaborative filtering
approaches in recommender systems. Nevertheless, useful latent user features can lead to …
approaches in recommender systems. Nevertheless, useful latent user features can lead to …
[PDF][PDF] Latent Feature Modelling for Recommender Systems
A Alhejaili, S Fatima - researchgate.net
Matrix factorization is one of the most successful model-based collaborative filtering
approaches in recommender systems. Nevertheless, useful latent user features can lead to …
approaches in recommender systems. Nevertheless, useful latent user features can lead to …