Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation

In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an academic and a commercial perspective. A fundamental aspect of music perception is that music is experienced in temporal context and in sequence. In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions. The model is learned online and is uniquely adapted for each listener. To reduce exploration time, DJ-MC exploits user feedback to initialize a model, which it subsequently updates by reinforcement. We evaluate our framework with human participants using both real song and playlist data. Our results indicate that DJ-MC's ability to recommend sequences of songs provides a significant improvement over more straightforward approaches, which do not take transitions into account.Comment: -Updated to the most recent and completed version (to be presented at AAMAS 2015) -Updated author list. in Autonomous Agents and Multiagent Systems (AAMAS) 2015, Istanbul, Turkey, May 201

Exploring Musical, Lyrical, and Network Dimensions of Music Sharing Among Depression Individuals

Depression has emerged as a significant mental health concern due to a variety of factors, reflecting broader societal and individual challenges. Within the digital era, social media has become an important platform for individuals navigating through depression, enabling them to express their emotional and mental states through various mediums, notably music. Specifically, their music preferences, manifested through sharing practices, inadvertently offer a glimpse into their psychological and emotional landscapes. This work seeks to study the differences in music preferences between individuals diagnosed with depression and non-diagnosed individuals, exploring numerous facets of music, including musical features, lyrics, and musical networks. The music preferences of individuals with depression through music sharing on social media, reveal notable differences in musical features and topics and language use of lyrics compared to non-depressed individuals. We find the network information enhances understanding of the link between music listening patterns. The result highlights a potential echo-chamber effect, where depression individual's musical choices may inadvertently perpetuate depressive moods and emotions. In sum, this study underscores the significance of examining music's various aspects to grasp its relationship with mental health, offering insights for personalized music interventions and recommendation algorithms that could benefit individuals with depression.Comment: arXiv admin note: text overlap with arXiv:2007.03137, arXiv:2205.03459 by other author

Procyon LLC: From Music Recommendations to Preference Mapping

Procyon LLC had re-launched and renamed their music discovery site, Electra, to Capella, in 2008. Its core strength had originated from Electra’s proprietary technology, which used music libraries from real people, its members, to generating “automated word-of-mouth” recommendations, targeted advertising and editorial content. With the re-launch, Capella’s focus changed from a business-to-consumer destination site to a demonstration site for Procyon as it pursued a new business-to-business strategy. What led Procyon to make this strategic change? What products and services should it market, and to whom? This case describes the transition from music recommendation to preference mapping, and provides students with a variety of alternative partnering options to consider as they move forward

The Impact of Spotify’s AI-Driven Music Recommender on User Listener Habits

This study explores how Spotify uses AI-technology to collect data about the user’s music listening behavior and serve personalized music recommendations based on their music taste and listening habits. It also involves a quantitative survey to discover the impact these AI- driven algorithms have on the Spotify users, especially focusing on four carefully chosen aspects: the user’s satisfaction with the music recommendations, the correlation between their satisfaction and their user activity, their selectivity in song choices and their ways of discovering new music. The results from the survey indicates that there is an overall satisfaction with the music personalization, especially for the most active users. Also, their reports indicate that they prefer the mix between familiarity and music discovery, and that they don’t believe the recommendations have a significant impact on their selectivity