scholarly journals Support the underground: characteristics of beyond-mainstream music listeners

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.

scholarly journals Exploiting Music Play Sequence for Music Recommendation

2017 ◽  
Author(s):  
Zhiyong Cheng ◽  
Jialie Shen ◽  
Lei Zhu ◽  
Mohan Kankanhalli ◽  
Liqiang Nie
Keyword(s):  
Recommender Systems ◽  
Feature Learning ◽  
Music Recommendation ◽  
Streaming Services ◽  
Rich Information ◽  
Music Recommender Systems ◽  
Public Datasets ◽  
Digital Footprints ◽  
Music Streaming ◽  
Music Play

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.

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

First Monday ◽  
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.

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

Information ◽  
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.

scholarly journals User models for multi-context-aware music recommendation

2020 ◽  
Author(s):  
Martin Pichl ◽  
Eva Zangerle
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Consumption ◽  
Current Context ◽  
Music Recommender Systems ◽  
Context Features ◽  
Content Information ◽  
Music Streaming

Abstract In the last decade, music consumption has changed dramatically as humans have increasingly started to use music streaming platforms. While such platforms provide access to millions of songs, the sheer volume of choices available renders it hard for users to find songs they like. Consequently, the task of finding music the user likes is often mitigated by music recommender systems, which aim to provide recommendations that match the user’s current context. Particularly in the field of music recommendation, adapting recommendations to the user’s current context is critical as, throughout the day, users listen to different music in numerous different contexts and situations. Therefore, we propose a multi-context-aware user model and track recommender system that jointly exploit information about the current situation and musical preferences of users. Our proposed system clusters users based on their situational context features and similarly, clusters music tracks based on their content features. By conducting a series of offline experiments, we show that by relying on Factorization Machines for the computation of recommendations, the proposed multi-context-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems.

scholarly journals A Closer-to-Reality Model for Comparing Relevant Dimensions of Recommender Systems, with Application to Novelty

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 500
Author(s):  
François Fouss ◽  
Elora Fernandes
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Evaluation Procedure ◽  
Recommendation Algorithm ◽  
Test Set ◽  
Ranking Algorithms ◽  
Traditional Training ◽  
Recommendation Algorithms ◽  
Recent Developments

Providing fair and convenient comparisons between recommendation algorithms—where algorithms could focus on a traditional dimension (accuracy) and/or less traditional ones (e.g., novelty, diversity, serendipity, etc.)—is a key challenge in the recent developments of recommender systems. This paper focuses on novelty and presents a new, closer-to-reality model for evaluating the quality of a recommendation algorithm by reducing the popularity bias inherent in traditional training/test set evaluation frameworks, which are biased by the dominance of popular items and their inherent features. In the suggested model, each interaction has a probability of being included in the test set that randomly depends on a specific feature related to the focused dimension (novelty in this work). The goal of this paper is to reconcile, in terms of evaluation (and therefore comparison), the accuracy and novelty dimensions of recommendation algorithms, leading to a more realistic comparison of their performance. The results obtained from two well-known datasets show the evolution of the behavior of state-of-the-art ranking algorithms when novelty is progressively, and fairly, given more importance in the evaluation procedure, and could lead to potential changes in the decision processes of organizations involving recommender systems.

scholarly journals Development of a Music Recommender System Based on Content Metadata Processing

2019 ◽  
Vol 17 (3) ◽  
pp. 43-60
Author(s):  
A. V. Menkin
Keyword(s):  
Recommender System ◽  
New Approach ◽  
Different Types ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Sparsity Problem ◽  
Metadata Processing ◽  
Essential Problem ◽  
Music Streaming ◽  
Tiny Part

Music recommender systems (MRS) help users of music streaming services to find interesting music in the music catalogs. The sparsity problem is an essential problem of MRS research. It refers to the fact that user usually rates only a tiny part of items. As a result, MRS often has not enough data to make a recommendation. To solve the sparsity problem, in this paper, a new approach that uses related items’ ratings is proposed. Hybrid MRS based on this approach is described. It uses tracks, albums, artists, genres normalized ratings along with information about relations between items of different types in the music catalog. The proposed MRS is evaluated and compared to collaborative method for users’ preferences prediction.

scholarly journals Immersed in Pop

2021 ◽  
Vol 33 (3) ◽  
pp. 84-103
Author(s):  
Zack Bresler
Keyword(s):  
Popular Music ◽  
Music Analysis ◽  
Music Technology ◽  
Subject Position ◽  
3D Sound ◽  
Subject Positioning ◽  
Streaming Services ◽  
First Time ◽  
The Relationship ◽  
Music Streaming

In 2019, Dolby Atmos Music launched on Tidal HiFi and Amazon Prime Music HD, integrating 3D sound with music streaming services for the first time. This is one of several recent incursions by popular music into immersive and interactive mediums, which also includes technologies such as virtual reality and 360-degree video. As immersive productions become more normal in popular music, they are increasingly critical to examine. How are aesthetic features of pop compositions altered or maintained in these productions? And how do these different spatial mediums affect compositional design, subject positioning, artists’ performativity, and staging? This article aims to address these questions by presenting a model for 3D music analysis that relates various notions of music technology and production to musicological concepts on performance environment, staging, and subject position. This model is then demonstrated in a hermeneutic close reading of the song ‘Blinding Lights’ by R&B superstar The Weeknd, which was released in both stereo and Dolby Atmos mediums in 2019. The goal of the analysis is to demonstrate some of the ways different mediums impinge on various interpretive stances and the relationship between the performer and listener in immersive popular music.

scholarly journals On Sampled Metrics for Item Recommendation (Extended Abstract)

2021 ◽  
Author(s):  
Walid Krichene ◽  
Steffen Rendle
Keyword(s):  
Experimental Study ◽  
Recommender Systems ◽  
Recent Work ◽  
Empirical Evaluation ◽  
Recommendation Algorithms ◽  
Speed Up ◽  
Better Than

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, e.g., recommender A is better than B, not even in expectation. We show that it is possible to improve the quality of the sampled metrics by applying a correction. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. Our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the estimates.

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