This paper reviews the research trends that link the advanced technical aspects of
recommendation systems that are used in various service areas and the business aspects of …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Recommender system usually faces popularity bias issues: from the data perspective, items
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …

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 …

The general aim of the recommender system is to provide personalized suggestions to
users, which is opposed to suggesting popular items. However, the normal training …

Recommendation models are usually trained on observational interaction data. However,
observational interaction data could result from users' conformity towards popular items …

Sequential recommendation problems have received increasing attention in research during
the past few years, leading to the inception of a large variety of algorithmic approaches. In …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

Recommender systems are known to suffer from the popularity bias problem: popular (ie
frequently rated) items get a lot of exposure while less popular ones are under-represented …