Data sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system conf...
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of item...
Current recommendation methods are mainly classified into contentbased, collaborative filtering and hybrid methods. These methods are based on similarity measurements among item...
Yi Cai, Ho-fung Leung, Qing Li, Jie Tang, Juanzi L...
Multiple-dimensional, i.e., polyadic, data exist in many applications, such as personalized recommendation and multipledimensional data summarization. Analyzing all the dimensions...
Finding relevant information within the vast amount of information exchanged via feeds is difficult. Previous research into this problem has largely focused on recommending relev...