Recommendations are meant to increase sales or ad revenue, as these are the first priority
of those who pay for them. As recommender systems match their recommendations with …

Algorithmic recommender systems are a ubiquitous feature of contemporary cultural life
online, suggesting music, movies, and other materials to their users. This article, drawing on …

User preferences are a crucial input needed by recommender systems to determine relevant
items. In single-shot recommendation scenarios such as content-based filtering and …

In the attention economy, video apps employ design mechanisms like autoplay that exploit
psychological vulnerabilities to maximize watch time. Consequently, many people feel a lack …

Meet the people who design the algorithms that capture our musical tastes. The people who
make music recommender systems have lofty goals: they want to broaden listeners' horizons …

Collaborative filtering algorithms find useful patterns in rating and consumption data and
exploit these patterns to guide users to good items. Many of the patterns in rating datasets …

Online platforms have a wealth of data, run countless experiments, and use industrial-scale
algorithms to optimize user experience. Despite this, many users seem to regret the time …

Personalized recommender systems have become indispensable in today's online world.
Most of today's recommendation algorithms are data-driven and based on behavioral data …

Recommender systems (RS) are on the rise in many domains. While they offer great
promises, they also raise concerns: lack of transparency, reduction of diversity, little to no …

Large language models (LLMs) have shown impressive capabilities in natural language
understanding and generation. Their potential for deeper user understanding and improved …