International audienceLatent 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
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural ap...
DoctorRecommender system has received significant attention from academia and various industries, es...
Recommender systems help people make decisions. They are particularly useful for product recommendat...
International audienceLatent factor models have been used widely in collaborative filtering based re...
Collaborative filtering (CF) is a widely used approach in recommender systems to solve many real-wor...
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Con...
In this paper, we propose a Bayesian Deep Collaborative Matrix Factorization (BDCMF) algorithm for c...
We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF ...
The Thirty-Fourth AAAI Conference on Artificial Intelligence: Interactive and Conversational Recomme...
The usage of Internet applications, such as social networking and e-commerce is increasing exponenti...
Extracting demographic features from hidden factors is an innovative concept that provides multiple ...
In this thesis, several Collaborative Filtering (CF) approaches with latent variable methods were st...
Recommender systems are an important kind of learning systems, which can be achieved by latent-facto...
Deep learning provides accurate collaborative filtering models to improve recommender system results...
Collaborative filtering (CF) has achieved great success in the field of recommender systems. In rece...
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural ap...
DoctorRecommender system has received significant attention from academia and various industries, es...
Recommender systems help people make decisions. They are particularly useful for product recommendat...
International audienceLatent factor models have been used widely in collaborative filtering based re...
Collaborative filtering (CF) is a widely used approach in recommender systems to solve many real-wor...
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Con...
In this paper, we propose a Bayesian Deep Collaborative Matrix Factorization (BDCMF) algorithm for c...
We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF ...
The Thirty-Fourth AAAI Conference on Artificial Intelligence: Interactive and Conversational Recomme...
The usage of Internet applications, such as social networking and e-commerce is increasing exponenti...
Extracting demographic features from hidden factors is an innovative concept that provides multiple ...
In this thesis, several Collaborative Filtering (CF) approaches with latent variable methods were st...
Recommender systems are an important kind of learning systems, which can be achieved by latent-facto...
Deep learning provides accurate collaborative filtering models to improve recommender system results...
Collaborative filtering (CF) has achieved great success in the field of recommender systems. In rece...
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural ap...
DoctorRecommender system has received significant attention from academia and various industries, es...
Recommender systems help people make decisions. They are particularly useful for product recommendat...