The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

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 systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …

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 …

As a highly data-driven application, recommender systems could be affected by data bias,
resulting in unfair results for different data groups, which could be a reason that affects the …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

Recommendation algorithms are known to suffer from popularity bias; a few popular items
are recommended frequently while the majority of other items are ignored. These …

Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates
systemic discrimination in automated decision‐making, providing opportunities to improve …

Contextual word embeddings such as BERT have achieved state of the art performance in
numerous NLP tasks. Since they are optimized to capture the statistical properties of training …

This article presents the first, systematic analysis of the ethical challenges posed by
recommender systems through a literature review. The article identifies six areas of concern …