The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Performing highly accurate representation learning on a high-dimensional and sparse
(HiDS) matrix is of great significance in a big data-related application such as a …

A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS)
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …

High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …

A recommender system (RS) is highly efficient in filtering people's desired information from
high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach …

How to accurately predict unknown quality-of-service (QoS) data based on observed ones is
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …

With the rapid development of services computing in the past decade, Quality-of-Service
(QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting …

Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns
hidden in such dynamic data for predicting missing ones with high accuracy. However …