Zhu‐Hong You

An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems

2015 · IEEE Transactions on Industrial Informatics

Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to …

A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

2015 · IEEE Transactions on Neural Networks and Learning Systems

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and …