Local low-rank matrix approximation 2017-02-16 11:35:28
Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is... ; || Joonseok Lee; Seungyeon Kim; Guy Lebanon...

Local Collaborative Ranking 2017-02-15 13:09:46
Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers... || recommender systems; collaborative filtering; ranking; || Joonseok Lee; Samy Bengio; Seungyeon Kim...

Listwise Collaborative Filtering 2017-02-15 11:10:58
Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional ... || Recommender systems; Collaborative filtering; Ranking- oriented collaborative filtering; 肯德尔; Kendall; Cross Entroy; 交叉熵; || Shanshan Huang; Shuaiqiang Wang; Tie-Yan Liu...

机器学习相关——协同过滤 2017-02-15 07:51:53
在现今的推荐技术和算法中,最被大家广泛认可和采用的就是基于协同过滤的推荐方法。本文将带你深入了解协同过滤的秘密。... || 推荐; 机器学习; 协同过滤; || 赵晨婷; 马春娥...