Recommendation systems are important business applications with significant economic impact. In recent years, a large number of algorithms have been proposed for recommendation systems. In this paper, we describe an open-source toolkit implementing many recommendation algorithms as well as popular e... || tootkit; datasets; explicit feedback; exrecommender systems; collaborative filtering; evaluation metric;库;开源软件; || Joonseok Lee; Mingxuan Sun; Guy Lebanon...
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...
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...
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...
