Music has a close relationship with people's emotion and mental status. Music
recommendation has both economic and social benefits. Unfortunately, most existing music …

More and more machine learning is applied to human behavior. Increasingly these
algorithms suffer from a hidden—but serious—problem. It arises because they often predict …

Evaluating algorithmic recommendations is an important, but difficult, problem. Evaluations
conducted offline using data collected from user interactions with an online system often …

Music creation is difficult because one must express one's creativity while following strict
rules. The advancement of deep learning technologies has diversified the methods to …

Natural interaction with recommendation and personalized search systems has received
tremendous attention in recent years. We focus on the challenge of supporting people's …

With the development of information technology, the subscription economy and streaming
service market have grown rapidly. In particular, music streaming services have disrupted …

The personalization of digital environments is becoming ubiquitous due to the rise of AI-
based algorithms and recommender systems. Arguably, this technological development has …

The consumption of music is increasingly reliant on the personalisation, recommendation,
and automated curation features of music streaming services. Using algorithm experience …

Technological advancements have created opportunities for entrepreneurship and
innovation and enabled the development of new products, services, platforms, and business …

The role of recommendation systems in the diversity of content consumption on platforms is
a much-debated issue. The quantitative state of the art often overlooks the existence of …