The music domain is among the most important ones for adopting recommender systems
technology. In contrast to most other recommendation domains, which predominantly rely on …

Abstract Reinforcement learning from Human Feedback (RLHF) learns from preference
signals, while standard Reinforcement Learning (RL) directly learns from reward signals …

News recommendation for anonymous readers is a useful but challenging task for many
news portals, where interactions between readers and articles are limited within a temporary …

In machine learning, the notion of multi-armed bandits refers to a class of online learning
problems, in which an agent is supposed to simultaneously explore and exploit a given set …

Fine-grained control over large language models (LLMs) remains a significant challenge,
hindering their adaptability to diverse user needs. While Reinforcement Learning from …

Reinforcement learning from Human Feedback (RLHF) learns from preference signals,
while standard Reinforcement Learning (RL) directly learns from reward signals …

Media services providers, such as music streaming platforms, frequently leverage swipeable
carousels to recommend personalized content to their users. However, selecting the most …

In streaming media applications, like music Apps, songs are recommended in a continuous
way in users' daily life. The recommended songs are played automatically although users …

In the field of music information retrieval (MIR), cover song identification (CSI) is a
challenging task that aims to identify cover versions of a query song from a massive …

The success of a cross-sectional systematic strategy depends critically on accurately ranking
assets prior to portfolio construction. Contemporary techniques perform this ranking step …