Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

Offline reinforcement learning (RL), a technology that offline learns a policy from logged data
without the need to interact with online environments, has become a favorable choice in …

Manipulation is a concern in many domains, such as social media, advertising, and
chatbots. As AI systems mediate more of our digital interactions, it is important to understand …

Over the years we have seen recommender systems shifting focus from optimizing short-
term engagement toward improving long-term user experience on the platforms. While …

Recent research has employed reinforcement learning (RL) algorithms to optimize long-term
user engagement in recommender systems, thereby avoiding common pitfalls such as user …

Since the inception of Recommender Systems (RS), the accuracy of the recommendations in
terms of relevance has been the golden criterion for evaluating the quality of RS algorithms …

Recommender systems are poised at the interface between stakeholders: for example, job
applicants and employers in the case of recommendations of employment listings, or artists …

In this paper, we propose a new privacy solution for the data used to train a recommender
system, ie, the user–item matrix. The user–item matrix contains implicit information, which …

Popularity bias is a prominent phenomenon in recommender systems (RS), especially in the
music domain. Although popularity bias mitigation techniques are known to enhance the …

Despite their proven various benefits, Recommender Systems can cause or amplify certain
undesired effects. In this paper, we focus on Popularity Bias, ie, the tendency of a …