Inferring User Expertise from Social Tagging in Music Recommender Systems for Streaming Services

Users leave digital footprints when interacting with various music streaming services. Music play sequence, which contains rich information about personal music preference and song similarity, has been largely ignored in previous music recommender systems. In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems. Towards the goal, we propose to use word embedding techniques in music play sequences to estimate the similarity between songs. The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery. Furthermore, the proposed method only considers the k-nearest songs (e.g., k = 5) in the learning process and thus avoids the increase of time complexity. Experimental results on two public datasets demonstrate that our methods could significantly improve the performance of both rating prediction and top-n recommendation tasks.

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‘Don’t mess with my algorithm’: Exploring the relationship between listeners and automated curation and recommendation on music streaming services

First Monday ◽

10.5210/fm.v27i1.11783 ◽

2022 ◽

Author(s):

Sophie Freeman ◽

Martin Gibbs ◽

Bjørn Nansen

Keyword(s):

Recommender Systems ◽

Participant Observation ◽

Music Listening ◽

Music Recommendation ◽

Structured Interviews ◽

Streaming Services ◽

Music Recommender Systems ◽

The Individual ◽

The Relationship ◽

Music Streaming

Given access to huge online collections of music on streaming platforms such as Spotify or Apple Music, users have become increasingly reliant on algorithmic recommender systems and automated curation and discovery features to find and curate music. Based on participant observation and semi-structured interviews with 15 active users of music streaming services, this article critically examines the user experience of music recommendation and streaming, seeking to understand how listeners interact with and experience these systems, and asking how recommendation and curation features define their use in a new and changing landscape of music consumption and discovery. This paper argues that through daily interactions with algorithmic features and curation, listeners build complex socio-technical relationships with these algorithmic systems, involving human-like factors such as trust, betrayal and intimacy. This article is significant as it positions music recommender systems as active agents in shaping music listening habits and the individual tastes of users.

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Context-Aware Music Recommender Systems for Groups: A Comparative Study

Information ◽

10.3390/info12120506 ◽

2021 ◽

Vol 12 (12) ◽

pp. 506

Author(s):

Adrián Valera ◽

Álvaro Lozano Murciego ◽

María N. Moreno-García

Keyword(s):

Comparative Study ◽

Recommender Systems ◽

Digital Libraries ◽

Context Aware ◽

The Past ◽

Single User ◽

Streaming Services ◽

Music Recommender Systems ◽

Music Streaming ◽

Group Recommender

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

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Support the underground: characteristics of beyond-mainstream music listeners

EPJ Data Science ◽

10.1140/epjds/s13688-021-00268-9 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Dominik Kowald ◽

Peter Muellner ◽

Eva Zangerle ◽

Christine Bauer ◽

Markus Schedl ◽

...

Keyword(s):

Popular Music ◽

Recommender Systems ◽

Demographic Characteristics ◽

User Models ◽

Recommendation Algorithms ◽

Streaming Services ◽

Music Recommender Systems ◽

Recommendation Accuracy ◽

Music Streaming

AbstractMusic recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup’s openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.

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