Add to ORKGWe present a Matrix Factorization(MF) based approach for the Netflix Prize competition. Currently MF based algorithms are popular and have proved successful for collaborative filtering tasks. For the Netflix Prize competition, we adopt three different types of MF algorithms: regularized MF, maximum margin MF and non-negative MF. Furthermore, for each MF algorithm, instead of selecting the optimal parameters, we combine the results obtained with several parameters. With this method, we achieve a performance that is more than 6% better than the Netflix‘s own system
In this paper, we consider collaborative filtering as a ranking problem. We present a method which u...
In this paper, we consider collaborative filtering as a ranking problem. We present a method which u...
This paper describes the solution method taken by LeBu-SiShu team for track1 in ACM KDD CUP 2011 con...
We present a Matrix Factorization(MF) based approach for the Netflix Prize competition. Currently MF...
Matrix factorization (MF) models have proved efficient and well scalable for collaborative filtering...
Many existing approaches to collaborative filtering can neither handle very large datasets nor easil...
Matrix-Factorization (MF) is an accurate and scalable approach for collaborative filtering (CF)-base...
International audienceWe introduce an asynchronous distributed stochastic gradient algorithm for mat...
Abstract. We propose a new approach for Collaborative filtering which is based on Boolean Matrix Fac...
Two major challenges for collaborative filtering problems are scalability and sparseness. Some power...
Proceedings of the 29th International Conference on Machine Learning, ICML 20121417-42
AbstractRecommendation Systems (RSs) are becoming tools of choice to select the online information r...
One of the leading approaches to collaborative filtering is to use matrix factorization to discover ...
Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain ...
In this paper, we consider collaborative filtering as a ranking problem. We present a method which u...
In this paper, we consider collaborative filtering as a ranking problem. We present a method which u...
In this paper, we consider collaborative filtering as a ranking problem. We present a method which u...
This paper describes the solution method taken by LeBu-SiShu team for track1 in ACM KDD CUP 2011 con...
We present a Matrix Factorization(MF) based approach for the Netflix Prize competition. Currently MF...
Matrix factorization (MF) models have proved efficient and well scalable for collaborative filtering...
Many existing approaches to collaborative filtering can neither handle very large datasets nor easil...
Matrix-Factorization (MF) is an accurate and scalable approach for collaborative filtering (CF)-base...
International audienceWe introduce an asynchronous distributed stochastic gradient algorithm for mat...
Abstract. We propose a new approach for Collaborative filtering which is based on Boolean Matrix Fac...
Two major challenges for collaborative filtering problems are scalability and sparseness. Some power...
Proceedings of the 29th International Conference on Machine Learning, ICML 20121417-42
AbstractRecommendation Systems (RSs) are becoming tools of choice to select the online information r...
One of the leading approaches to collaborative filtering is to use matrix factorization to discover ...
Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain ...
In this paper, we consider collaborative filtering as a ranking problem. We present a method which u...
In this paper, we consider collaborative filtering as a ranking problem. We present a method which u...
In this paper, we consider collaborative filtering as a ranking problem. We present a method which u...
This paper describes the solution method taken by LeBu-SiShu team for track1 in ACM KDD CUP 2011 con...