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

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 …

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

Scholars in algorithmic recommender systems research have developed a largely
standardized scientific method, where progress is claimed by showing that a new algorithm …

Recommender systems are efficient tools for filtering online information, which is
widespread owing to the changing habits of computer users, personalization trends, and …

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

Offline evaluation is an essential complement to online experiments in the selection,
improvement, tuning, and deployment of recommender systems. Offline methodologies for …

Recommender systems are among today's most successful application areas of artificial
intelligence. However, in the recommender systems research community, we have fallen …

When the Netflix Prize launched in 2006, it put a spotlight on the importance and use of
recommender systems in real-world applications. The competition provided many lessons …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …