Mindful Music Listening: Fostering Wellbeing on Music Streaming Platforms through Positive AI

Recommender systems have become a core strategy of countless online sites and platforms including music streaming services like Spotify. These recommender systems are associated with limiting our exposure to a narrower range of content, this is even the case for features designed to promote discovery like Spotify’s Discover Weekly. Discover...

Contextual Personalized Re-Ranking of Music Recommendations through Audio Features Based User Preference Models

With advancements in Internet and technology, it has become increasingly easy for people to enjoy music. Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs created a need for relevant song recommendations. Music recommender systems assist users in finding...

Understanding the relationship between user emotion and latent musical features

With the advent of Internet and resulting data boom, Recommender Systems have come to rescue by filtering the information available on the internet by providing us with relevant information. These systems come handy when one wants to listen to songs, watch movies or even buy products on the Internet. Primarily, these recommender systems used...

Breaking the filter-bubble: Using visualizations to encourage blind-spots exploration

In recent years, personalized recommender systems have been facing criticism in research due to their ability to trap users in their circle of choices, called "filter-bubble", thereby limiting their exposure to novel content. In solving the issue of filter-bubble, past research has focused on providing explanations to users about how a...

Enhancing the Diversity Adjusting Strategy with Personality Information in Music Recommender Systems

Current research on personality and diversity based Recommender Systems (RecSys) are mostly separated. In most diversity-based Recommender Systems, researchers usually endeavored to achieve an optimal balance between accuracy and diversity while they commonly set a same diversity level for all users. Different diversity needs for users with...