The Music Streaming Sessions Dataset

At the core of many important machine learning problems faced by online streaming services is a need to model how users interact with the content. These problems can often be reduced to a combination of 1) sequentially recommending items to the user, and 2) exploiting the user's interactions with the items as feedback for the machine learning model. Unfortunately, there are no public datasets currently available that enable researchers to explore this topic. In order to spur that research, we release the Music Streaming Sessions Dataset (MSSD), which consists of approximately 150 million listening sessions and associated user actions. Furthermore, we provide audio features and metadata for the approximately 3.7 million unique tracks referred to in the logs. This is the largest collection of such track metadata currently available to the public. This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.Comment: 3 pages, introducing a new large scale datase

Investigating bias in Music Recommender Systems

Music Recommender Systems (MRS) are software applications that provide personalized music recommendations based on user preferences and listening history. They analyze data to suggest music that aligns with individual tastes, enhancing the music discovery experience. This thesis aims to investigate the influence of record labels across different music recommendation datasets and evaluate their impact on recommender systems. Additionally, it seeks to expand the scope and experimentation of prior research on bias within feedback loops of MRS. To study their effect, the datasets are preprocessed and fed into a multi-stage web crawler that retrieves record label information for individual albums as well as an assignment to a major record company (Universal, Sony, Warner) or independent. This crawler is used to enrich our dataset collection. Based on the additional information, we can show different characteristics and identify particular biases in their user-generated music collections of playlists and listening profiles. Moreover, recommender system experiments are conducted, presenting results of feedback loop simulations, where the stability of record label distribution in longitudinal recommendations are studied. All findings and gathered record label information are made publicly available to the research community.Els Sistemes de Recomanació Musical (MRS) són aplicacions de software que proporcionen recomanacions de música personalitzades basades en les preferències i el històric d'escolta de l'usuari. Analitzen dades per suggerir música que s'ajusti als gustos individuals, millorant així l'experiència de descobriment musical. Aquesta tesi té com a objectiu investigar la influència de les discogràfiques en diferents conjunts de dades de recomanació musical i avaluar el seu impacte en els sistemes de recomanació. A més, busca ampliar l'abast i l'experimentació de recerques prèvies sobre biaixos en els bucles de retroalimentació dels MRS. Per estudiar el seu efecte, els conjunts de dades es pre-processen i s'insereixen a un rastrejador web de diverses etapes que recopila informació sobre les discogràfiques dels àlbums individuals, així com la seva classificació en una discogràfica principal (Universal, Sony, Warner) o independent. Aquest rastrejador s'utilitza per enriquir la nostra col·lecció de dades. Basant-nos en la informació addicional, podem mostrar diferents característiques i identificar biaixos particulars en les col·leccions de música generades pels usuaris, com ara llistes de reproducció i perfils d'escolta. A més, es fan experiments en un entorn simulat de recomanacions, presentant els primers resultats de la simulació de bucles de retroalimentació on s'estudia l'estabilitat de la distribució de segells discogràfics en recomanacions longitudinals. Totes les troballes i la informació recopilada de segells discogràfics es posa a la disposició del públic per a la comunitat investigadora

Leveraging Microblogs for Spatiotemporal Music Information Retrieval

Abstract. We present results of text data mining experiments for music retrieval, analyzing microblogs gathered from November2011 to September 2012 to infer music listening patterns all around the world. We assess relationships between particular music preferences and spatial properties, such as month, weekday, and country, and the temporal stability of listening activities. The findings of our study will help improve music retrieval and recommendation systems in that it will allow to incorporate geospatial and cultural information into models for music retrieval, which has not been looked into before.