Related papers: Multiple Stakeholders in Music Recommender Systems

Recommender systems are a valuable tool for software engineers. For example, they can provide developers with a ranked list of files likely to contain a bug, or multiple auto-complete suggestions for a given method stub. However, the way…

Recommender engines have become an integral component in today's e-commerce systems. From recommending books in Amazon to finding friends in social networks such as Facebook, they have become omnipresent. Generally, recommender systems can…

Most modern recommendation algorithms are data-driven: they generate personalized recommendations by observing users' past behaviors. A common assumption in recommendation is that how a user interacts with a piece of content (e.g., whether…

Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content recommendation platforms. This burgeoning competition reshapes…

This work presents a user-centric recommendation framework, designed as a pipeline with four distinct, connected, and customizable phases. These phases are intended to improve explainability and boost user engagement. We have collected the…

Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services. However, extending playlists while preserving their musical characteristics and matching user preferences remains a…

AI tools increasingly shape how we discover, make and experience music. While these tools can have the potential to empower creativity, they may fundamentally redefine relationships between stakeholders, to the benefit of some and the…

Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems. As a first step toward achieving that goal, we propose a model that observes in real time the diversity brought by…

Serendipity-oriented recommender systems expose users to unfamiliar items to counter filter bubbles, yet mere exposure does not ensure that users will understand or appreciate the content they encounter. We propose Peer Recommendation, a…

Recommender systems rely heavily on user feedback to learn effective user and item representations. Despite their widespread adoption, limited attention has been given to the uncertainty inherent in the feedback used to train these systems.…

Recommender systems remain underutilized in humanities and historical research, despite their potential to enhance the discovery of cultural records. This paper offers an initial value identification of the multiple stakeholders that might…

This paper is motivated by the observation that, in many cases, we do not need to serve specific messages, but rather, any message within a content-type. Content-type traffic pervades a host of applications today, ranging from search…

News diversity in the media has for a long time been a foundational and uncontested basis for ensuring that the communicative needs of individuals and society at large are met. Today, people increasingly rely on online content and…

Musical professionals who produce material for non-musical stakeholders often face communication challenges in the early ideation stage. Expressing musical ideas can be difficult, especially when domain-specific vocabulary is lacking. This…

The problem of ranking is a multi-billion dollar problem. In this paper we present an overview of several production quality ranking systems. We show that due to conflicting goals of employing the most effective machine learning models and…

Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking stages, predominantly rely on collaborative filtering, which fails to exploit the…

Recommender systems are nowadays a pervasive part of our online user experience, where they either serve as information filters or provide us with suggestions for additionally relevant content. These systems thereby influence which…

The automated generation of music playlists can be naturally regarded as a sequential task, where a recommender system suggests a stream of songs that constitute a listening session. In order to predict the next song in a playlist, some of…

Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify interesting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior…

The fintech PicPay offers a wide range of financial services to its 30 million monthly active users, with more than 50 thousand items recommended in the PicPay mobile app. In this scenario, promoting specific items that are strategic to the…