The ability of humans to create and disseminate culture is often credited as the single most
important factor of our success as a species. In this Perspective, we explore the notion of …

The original filter bubble thesis states that the use of personalization algorithms results in a
unique universe of information for each of us, with far-reaching individual and societal …

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 …

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 …

In traditional recommender system literature, diversity is often seen as the opposite of
similarity, and typically defined as the distance between identified topics, categories or word …

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 …

We present results from a large-scale, randomized field experiment on Spotify testing the
effect of personalized recommendations on consumption diversity. In the experiment, both …

Reinforcement Learning (RL) has been sought after to bring next-generation recommender
systems to further improve user experience on recommendation platforms. While the …

This chapter gives an introduction to music recommender systems, considering the unique
characteristics of the music domain. We take a user-centric perspective, by organizing our …

Recommendation system often suffers from popularity bias. Often the training data inherently
exhibits long-tail distribution in item popularity (data bias). Moreover, the recommendation …