The large array of recommendation algorithms proposed over the years brings a challenge
in reproducing and comparing their performance. This paper introduces an open-source …

Matrix approximation is a common tool in recommendation systems, text mining, and
computer vision. A prevalent assumption in constructing matrix approximations is that the …

The era of Big Data analytics has begun in most industries within developed countries. This
new analytics tool has raised motivation for experts and researchers to study its impacts to …

Personalized recommendation systems are used in a wide variety of applications such as
electronic commerce, social networks, web search, and more. Collaborative filtering …

Matrix approximation is a common tool in recommendation systems, text mining, and
computer vision. A prevalent assumption in constructing matrix approximations is that the …

When building a recommender system, how can we ensure that all items are modeled well?
Classically, recommender systems are built, optimized, and tuned to improve a global …

In the past decades, recommendation systems have provided lots of valuable personalized
suggestions for the users to address the problem of information over-loaded. Collaborative …

E-commerce organizations are growing exponentially with time in terms of both business
and data. Many organizations rely on these websites to attract new customers and retain the …

Robust machine learning is an increasingly important topic that focuses on developing
models resilient to various forms of imperfect data. Due to the pervasiveness of …

As a powerful data modeling tool, Heterogeneous Information Network (HIN) has been
successfully used in auxiliary information exploitation to boost recommendation …