On the Importance of Considering Country-specific Aspects on the Online-Market: An Example of Music Recommendation Considering Country-Specific Mainstream

In the field of music recommender systems, country-specific aspects have received little attention, although it is known that music perception and preferences are shaped by culture; and culture varies across countries. Based on the LFM-1b dataset (including 53,258 users from 47 countries), we show that there are significant country-specific differences in listeners’ music consumption behavior with respect to the most popular artists listened to. Results indicate that, for instance, Finnish users’ listening behavior is farther away from the global mainstream, while United States’ listeners are close to the global mainstream. Relying on rating prediction experiments, we tailor recommendations to a user’s level of preference for mainstream (defined on a global level and on a country level) and the user’s country. Results suggest that, in terms of rating prediction accuracy, a combination of these two filtering strategies works particularly well for users of countries far away from the global mainstream

Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations

In this paper, we introduce a psychology-inspired approach to model and predict the music genre preferences of different groups of users by utilizing human memory processes. These processes describe how humans access information units in their memory by considering the factors of (i) past usage frequency, (ii) past usage recency, and (iii) the current context. Using a publicly available dataset of more than a billion music listening records shared on the music streaming platform Last.fm, we find that our approach provides significantly better prediction accuracy results than various baseline algorithms for all evaluated user groups, i.e., (i) low-mainstream music listeners, (ii) medium-mainstream music listeners, and (iii) high-mainstream music listeners. Furthermore, our approach is based on a simple psychological model, which contributes to the transparency and explainability of the calculated predictions.Comment: Dominik Kowald and Elisabeth Lex contributed equally to this wor

Support the Underground: Characteristics of Beyond-Mainstream Music Listeners

Music 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.Comment: Accepted for publication in EPJ Data Science - link to published version will be adde

An empirical investigation into music listening behaviour in the presence of the network effect

The rapid expansion of online platforms has revolutionised the digital media industry, transforming the way people consume digital content and interact with each other and the platforms. The network effects (NEs) play a vital role in the success of online platforms, fostering user collaboration and interest exchange, thereby creating a positive feedback loop that influences user behaviours and contributes to a platform’s success. However, initial studies exploring the NEs phenomenon primarily focused on network size, predating the widespread adoption of online platforms, and thus providing little insight into the application of NEs in the online platform context. Furthermore, despite extensive studies on online platforms, established theoretical constructs and practical frameworks that integrate other variables contributing to NEs in online platforms are lacking. The thesis consists of three research chapters that significantly contribute to the study of NEs and their influence on users’ online music listening behaviours. In the first study, a systematic and rigorous approach was adopted to develop an NEs measurement scale. Drawing on social network and social action theories, we developed a novel NEs model with two subconstructs: social network structure and social action. An empirical research design was applied using the data of 200 Last.fm users. We employed a combination of partial least squares (PLS) path modelling and an expert focus group to validate the model. The results supported the validity and reliability of the developed NEs model. The second study addressed the scarcity of longitudinal analysis related to the evolving nature of NEs and the lack of empirical research to measure the impact of NEs on online music listening behaviours. We examined the NEs construct from our first study to show the impact of NEs on Last.fm users’ music listening behaviours cross-sectionally and longitudinally. The research method used was partial least square-structural equation modelling (PLS-SEM) of data obtained from Last.fm within two time intervals, targeting 1,708 users. Our study found that NEs positively influence users’ music listening behaviours, including the quantity, variety, and novelty of their music consumption. Specifically, the multigroup analysis revealed that the positive impact of NEs on users’ music listening behaviours becomes stronger over time. Furthermore, as the social network structure strengthens and users engage in more social actions, there is a carryover effect on NEs at subsequent times. The third study explored the impact of COVID-19 on online music listening behaviours in relation to listeners’ social interactions. We analysed the online music listening behaviours and social interactions of 37,328 Last.fm users in 45 countries before and after the first wave of confinement, using robust causal inference methods: difference in differences (DiD) and two-way fixed effects (TWFE). The results revealed that, in response to COVID-19, there was a decline in the quantity, variety, and novelty of music consumption, with a shift towards mainstream artists. However, our analysis also found that users with more online social connections and communications exhibited the opposite behaviour. This study provides guidance for the development of innovative design strategies for digital media, including music, movies, and games

A Stakeholder-Centered View on Fairness in Music Recommender Systems

Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively little attention in this regard. However, addressing fairness of music recommender systems (MRSs) is highly important because the performance of these systems considerably impacts both the users of music streaming platforms and the artists providing music to those platforms. The distinct needs that these stakeholder groups may have, and the different aspects of fairness that therefore should be considered, make for a challenging research field with ample opportunities for improvement. The review first outlines current literature on MRS fairness from the perspective of each stakeholder and the stakeholders combined, and then identifies promising directions for future research. The two open questions arising from the review are as follows: (i) In the MRS field, only limited data is publicly available to conduct fairness research; most datasets either originate from the same source or are proprietary (and, thus, not widely accessible). How can we address this limited data availability? (ii) Overall, the review shows that the large majority of works analyze the current situation of MRS fairness, whereas only few works propose approaches to improve it. How can we move forward to a focus on improving fairness aspects in these recommender systems? At FAccTRec '22, we emphasize the specifics of addressing RS fairness in the music domain

Fairness in music recommender systems: a stakeholder-centered mini review

The performance of recommender systems highly impacts both music streaming platform users and the artists providing music. As fairness is a fundamental value of human life, there is increasing pressure for these algorithmic decision-making processes to be fair as well. However, many factors make recommender systems prone to biases, resulting in unfair outcomes. Furthermore, several stakeholders are involved, who may all have distinct needs requiring different fairness considerations. While there is an increasing interest in research on recommender system fairness in general, the music domain has received relatively little attention. This mini review, therefore, outlines current literature on music recommender system fairness from the perspective of each relevant stakeholder and the stakeholders combined. For instance, various works address gender fairness: one line of research compares differences in recommendation quality across user gender groups, and another line focuses on the imbalanced representation of artist gender in the recommendations. In addition to gender, popularity bias is frequently addressed; yet, primarily from the user perspective and rarely addressing how it impacts the representation of artists. Overall, this narrative literature review shows that the large majority of works analyze the current situation of fairness in music recommender systems, whereas only a few works propose approaches to improve it. This is, thus, a promising direction for future research

A content-based music recommender system

Music recommenders have become increasingly relevant due to increased accessibility provided by various music streaming services. Some of these streaming services, such as Spotify, include a recommender system of their own. Despite many advances in recommendation techniques, recommender systems still often do not provide accurate recommendations. This thesis provides an overview of the history and developments of music information retrieval from a more content-based perspective. Furthermore, this thesis describes recommendation as a problem and the methods used for music recommendation with special focus on content-based recommendation by providing detailed descriptions on the audio content features and content-based similarity measures used in content-based music recommender systems. Some of the presented features are used in our own content-based music recommender. Both objective and subjective evaluation of the implemented recommender system further confirm the findings of many researchers that music recommendation based solely on audio content does not provide very accurate recommendations

Metric Optimization and Mainstream Bias Mitigation in Recommender Systems

The first part of this thesis focuses on maximizing the overall recommendation accuracy. This accuracy is usually evaluated with some user-oriented metric tailored to the recommendation scenario, but because recommendation is usually treated as a machine learning problem, recommendation models are trained to maximize some other generic criteria that does not necessarily align with the criteria ultimately captured by the user-oriented evaluation metric. Recent research aims at bridging this gap between training and evaluation via direct ranking optimization, but still assumes that the metric used for evaluation should also be the metric used for training. We challenge this assumption, mainly because some metrics are more informative than others. Indeed, we show that models trained via the optimization of a loss inspired by Rank-Biased Precision (RBP) tend to yield higher accuracy, even when accuracy is measured with metrics other than RBP. However, the superiority of this RBP-inspired loss stems from further benefiting users who are already well-served, rather than helping those who are not. This observation inspires the second part of this thesis, where our focus turns to helping non-mainstream users. These are users who are difficult to recommend to either because there is not enough data to model them, or because they have niche taste and thus few similar users to look at when recommending in a collaborative way. These differences in mainstreamness introduce a bias reflected in an accuracy gap between users or user groups, which we try to narrow.Comment: PhD Thesis defended on Nov 14, 202