With tremendous amount of recommendation algorithms proposed every year, one critical
issue has attracted a considerable amount of attention: there are no effective benchmarks for …

In recommender systems, top-N recommendation is an important task with implicit feedback
data. Although the recent success of deep learning largely pushes forward the research on …

Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

There has been sustained interest from both academia and industry throughout the years
due to the importance and practicability of recommendation systems. However, several …

Fairness in the recommendation domain has recently attracted increasing attention due to
more and more concerns about the algorithm discrimination and ethics. While recent years …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

Deep recommender systems have achieved remarkable improvements in recent years.
Despite its superior ranking precision, the running efficiency and memory consumption turn …

The progress of recommender systems is hampered mainly by evaluation as it requires real-
time interactions between humans and systems, which is too laborious and expensive. This …

Algorithms that create recommendations based on observed data have significant
commercial value for online retailers and many other industries. Recommender systems …

Recommender systems research is being slowed by the difficulty of replicating and
comparing research results. Published research uses various experimental methodologies …