Add to ORKGInternational audienceWe propose an original approach based on a new formulation of a recommender system using state space description for users and web resources. Then states and parameters are predicted and estimated with two stages algorithms of a Kalman filter. In this paper, we give the main theoretical results of this original approach
The focus of present research is widely used news recommendation techniques such as “most popular ” ...
The term filter or estimator is commonly used to refer to sys-tems that extract information about a ...
Recommender systems are algorithms that suggest content or products to users on the internet. These ...
International audienceThis position paper proposes an original approach based on a new formulation o...
In this paper, we propose a new approach for recommender systems based on target tracking by Kalman ...
International audienceRecommender systems provide users with pertinent resources according to their ...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
Abstract: The paper deals with a factorized version of Kalman filter. Via factorization of a state-s...
Abstract: The paper deals with a factorized version of Kalman filter. Via factorization of a state-s...
Support in R for state space estimation via Kalman filtering was limited to one package, until fairl...
Nowadays, recommendation systems are used successfully to provide items (example: movies, music, boo...
International audienceA recommender system based on semantic web technologies and on an adaptive hyp...
This new edition presents a thorough discussion of the mathematical theory and computational schemes...
State space model is a class of models where the observations are driven by underlying stochastic pr...
The focus of present research is widely used news recommendation techniques such as “most popular ” ...
The term filter or estimator is commonly used to refer to sys-tems that extract information about a ...
Recommender systems are algorithms that suggest content or products to users on the internet. These ...
International audienceThis position paper proposes an original approach based on a new formulation o...
In this paper, we propose a new approach for recommender systems based on target tracking by Kalman ...
International audienceRecommender systems provide users with pertinent resources according to their ...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
Abstract: The paper deals with a factorized version of Kalman filter. Via factorization of a state-s...
Abstract: The paper deals with a factorized version of Kalman filter. Via factorization of a state-s...
Support in R for state space estimation via Kalman filtering was limited to one package, until fairl...
Nowadays, recommendation systems are used successfully to provide items (example: movies, music, boo...
International audienceA recommender system based on semantic web technologies and on an adaptive hyp...
This new edition presents a thorough discussion of the mathematical theory and computational schemes...
State space model is a class of models where the observations are driven by underlying stochastic pr...
The focus of present research is widely used news recommendation techniques such as “most popular ” ...
The term filter or estimator is commonly used to refer to sys-tems that extract information about a ...
Recommender systems are algorithms that suggest content or products to users on the internet. These ...