Recommender systems use past behaviors of users to suggest items. Most tend to offer
items similar to the items that a target user has indicated as interesting. As a result, users …

Novelty and diversity have been identified, along with accuracy, as prominent properties of
useful recommendations. Considerable progress has been made in the field in terms of the …

This report identifies two main bodies of academic research that pay sustained attention to
algorithmic recommendation in the realm of culture: a) academic computer science and b) …

Large language models (LLMs) powered conversational search systems have already been
used by hundreds of millions of people, and are believed to bring many benefits over …

Recommender systems are filters which suggest items or information that might be
interesting to users. These systems analyze the past behavior of a user, build her profile that …

Purpose Serendipity is an interesting phenomenon to study in information science as it plays
a fundamental–but perhaps underestimated–role in how we discover, explore, and learn in …

Over the past several years, research in recommender systems has emphasized the
importance of serendipity, but there is still no consensus on the definition of this concept and …

In recommending to another individual an item that one loves, accuracy is important,
however in most cases, focusing only on accuracy generates less satisfactory …

Previous studies on exploration have shown that users can be nudged to explore further
away from their current preferences. However, these effects were shown in a single session …

Recommender systems are efficient at predicting users' current preferences, but how users'
preferences develop over time is still under-explored. In this work, we study the development …