Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

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 …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

The rapid increase of the data of user behaviors on the Internet brings a promising chance to
better discover user preferences. Recommender systems have become a popular tool for …

Research on recommendation systems is swiftly producing an abundance of novel methods,
constantly challenging the current state-of-the-art. Inspired by advancements in many …

Collaborative filtering is a technique that takes into account the common characteristics of
users and items in recommender systems. Matrix decompositions are one of the most used …

With the rapid development of Artificial Intelligence of Things (AIoT), many applications are
developed and deployed, especially mobile applications and edge applications. Many …

We review popular unsupervised learning methods for the analysis of high‐dimensional
data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks …

This article introduces new multiplicative updates for nonnegative matrix factorization with
the-divergence and sparse regularization of one of the two factors (say, the activation …

Order is one of the main instruments to measure the relationship between objects in
(empirical) data. However, compared to methods that use numerical properties of objects …