Recommender systems provide personalized information access to users of Internet
services from social networks to e-commerce to media and entertainment. As is appropriate …

There exist situations of decision-making under information overload in the Internet, where
people have an overwhelming number of available options to choose from, eg products to …

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

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Recent work on machine learning has begun to consider issues of fairness. In this paper, we
extend the concept of fairness to recommendation. In particular, we show that in some …

Fairness has emerged as an important category of analysis for machine learning systems in
some application areas. In extending the concept of fairness to recommender systems, there …

In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …

Person-job fit is the core technique of online recruitment platforms, which can improve the
efficiency of recruitment by accurately matching the job positions with the job seekers …

The goal of this tutorial is to expose participants to the current research on social
recommender systems (ie, recommender systems for the social web). Participants will …

There is growing research interest in recommendation as a multi-stakeholder problem, one
where the interests of multiple parties should be taken into account. This category subsumes …