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Genre-based Analysis of Social Media Data on Music Listening Behavior [ Are Fans of Classical Music Really Averse to Social Media ?]
Abstract
It is frequently presumed that lovers of Classical music are not present in social media. In this paper, we investigate whether this statement can be empirically verified. To this end, we compare two social media platforms - Last.fm and Twitter - and perform a study on musical preference of their respective users. We investigate two research hypotheses: (i) Classical music fan are more reluctant to use social media to indicate their listing habits than listeners of other genres and (ii) there are correlations between the use of Last.fm and Twitter to indicate music listening behavior. Both hypotheses are verified and substantial differences could be made out for Twitter users. The results of these investigations will help improve music recommendation systems for listeners with non-mainstream music taste.
... Classical music consumption, especially live performances, did not change much over the recent decades, despite the development of new technologies. The share of consumption on mobile devices and the buzz on social media is very lim- ited [14]. In order to provide the listeners of classical music an with an enhanced experience that new technologies can offer (such as additional information and personalization) and lower the barriers for engagement with classical music [9], we performed a series of studies and implemented a set 1 http://phenicx.upf.edu ...
... In order to implement personalized applications, such as adaptation of user interfaces [10], for end users the system should be able to acquire the user preferences. Implicit acquisition of such data in the domain of classical music has shown to be hard as users hardly leave any traces on their preferences about classical music on social media [14] . However , related work suggests that preferences are related to two proxy constructs, personality and music sophistication. ...
This paper reports on three demonstrators developed to provide personalized, enhanced experiences of classical music, within the EU FP7 project "Performances as Highly Enriched aNd Interactive Concert eXperiences" (PHENICX): (i) an interface for accessing supplemental multimodal material about music items, (ii) a recommender system for visualizations of classical music, and (iii) a recommender system for tagging. The personalization in all three demos is achieved through modeling users' personality and musical sophistication. The links to the web interfaces are provided as well as the outcomes of quantitative and qualitative evaluations.
... Chen and Shen [2] propose a recommendation approach that integrates user location, listening history, music descriptors, and global music popularity trends inferred from microblogs. In this work, we chose the music platform Last.fm to gather a real-world dataset, since it has been shown to attract users of a wide variety of music tastes [9]. In contrast, existing work commonly makes use of rather small and noisy datasets, typically gathered from Twitter and including a maximum of a few million listening events [6]. ...
... Age: Listeners from 8 possible age groups are considered. These ranges are67891011121314151617,18192021,22232425,2627282930,31323334353637383940,41424344454647484950,51525354555657585960,: US age [Start-End]. Gender: A listener's gender is considered (i.e. ...
We investigate a range of music recommendation algorithm combinations, score aggregation functions, normalization techniques, and late fusion techniques on approximately 200 million listening events collected through Last.fm. The overall goal is to identify superior combinations for the task of artist recommendation. Hypothesizing that user characteristics influence performance on these algorithmic combinations, we consider specific user groups determined by age, gender, country, and preferred genre. Overall, we find that the performance of music recommendation algorithms highly depends on user characteristics.
... users in our dataset toward certain genres differ from the genre preferences of the population at large. For instance, we found that rap and R&B as well as classical music is substantially underrepresented in Last.fm listening data (Schedl and Tkalcic, 2014), which we use in the present study. To some extent, these limitations related to the dataset could be alleviated in the future by performing further data cleansing and preprocessing steps, e.g., threshold-based filtering of exorbitant playcounts by a minority of listeners. ...
Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the “music mainstream” strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user’s country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.
... users in our dataset towards certain genres differ from the genre preferences of the population at large. For instance, we found that rap and R&B as well as classical music is substantially underrepresented in Last.fm listening data [68], which we use in the present study. To some extent, these limitations related to the dataset could be alleviated in the future by performing further data cleansing and preprocessing steps, e.g., threshold-based filtering of exorbitant playcounts by a minority of listeners. ...
Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.
... Similarly, the dataset is known to be biased with respect to gender (with a high percentage of male users in the sample). Listeners of classical music tend to be underrepresented [84], whereas listeners of the genres metal and alternative tend to be overrepresented on the Last.fm platform [85]. ...
Relevance:
Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. These approaches recommend to the target user what is currently popular among all users of the system. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music preferences far away from the global music mainstream. Addressing this gap, the contribution of this article is three-fold.
Definition of mainstreaminess measures:
First, we provide several quantitative measures describing the proximity of a user's music preference to the music mainstream. Assuming that there is a difference between the global music mainstream and a country-specific one, we define the measures at two levels: relating a listener's music preferences to the global music preferences of all users, or relating them to music preferences of the user's country. To quantify such music preferences, we define a music item's popularity in terms of artist playcounts (APC) and artist listener counts (ALC). Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. This eventually results in a framework of 6 measures to quantify music mainstream.
Differences between countries with respect to music mainstream:
Second, we perform in-depth quantitative and qualitative studies of music mainstream in that we (i) analyze differences between countries in terms of their level of mainstreaminess, (ii) uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), analyzing these with a mixed-methods approach, and (iii) investigate differences between countries in terms of listening preferences related to popular music artists. We conduct our studies and experiments using the standardized LFM-1b dataset, from which we analyze about 800,000,000 listening events shared by about 53,000 users (from 47 countries) of the music streaming platform Last.fm. We show that there are substantial country-specific differences in listeners' music consumption behavior with respect to the most popular artists listened to.
Rating prediction experiments:
Third, we demonstrate the applicability of our study results to improve music recommendation systems. To this end, we conduct rating prediction experiments in which we tailor recommendations to a user's level of preference for the music mainstream using the proposed 6 mainstreaminess measures: defined by a distribution-based or rank-based approach, defined on a global level or on a country level (for the user's country), and for APC or ALC. Our approach roughly equals a hybrid recommendation approach in which a demographic filtering strategy is implemented before collaborative filtering is performed. Results suggest that, in terms of rating prediction accuracy, each of the presented mainstreaminess definitions has its merits.
... Other analyses could model music discovery or preference by considering specific geographies, musical genres, or even individual users. Large-scale data have been used to address specific musical questions including the long tail in music-related microblogs , social media behavior of Classical music fans (Schedl and Tkalčič, 2014), the relationship between musical taste and personality factors (Bansal and Woolhouse, 2015), and Twitter activity around a specific musical event (Iren et al., 2016). ...
Music discovery in everyday situations has been facilitated in recent years by audio content recognition services such as Shazam. The widespread use of such services has produced a wealth of user data, specifying where and when a global audience takes action to learn more about music playing around them. Here, we analyze a large collection of Shazam queries of popular songs to study the relationship between the timing of queries and corresponding musical content. Our results reveal that the distribution of queries varies over the course of a song, and that salient musical events drive an increase in queries during a song. Furthermore, we find that the distribution of queries at the time of a song's release differs from the distribution following a song's peak and subsequent decline in popularity, possibly reflecting an evolution of user intent over the “life cycle” of a song. Finally, we derive insights into the data size needed to achieve consistent query distributions for individual songs. The combined findings of this study suggest that music discovery behavior, and other facets of the human experience of music, can be studied quantitatively using large-scale industrial data.
Over the past millennia, music has actively been performed and listened to by mankind, thus also playing an important role in establishing sociocultural identities that have evolved over time. In parallel, for many centuries, newspapers played an important role in informing society on a regular and frequent basis on topics noteworthy at that time. Therefore, in retrospect, these newspapers offer windows into historic topics of sociocultural significance, including cultural and musical life. Thanks to ongoing digitization efforts, large-scale newspaper corpora now have become broadly available and accessible. Taking the digitized historical newspaper collection of the National Library of The Netherlands as an example, in this paper, we discuss how considering music-related mentionings in newspapers can enable potential new research directions and questions. We discuss open syntactic and semantic data-related technical challenges when analyzing music-related mentionings in digitized historical newspaper collections. Finally, we discuss how successful detection of music-related mentionings can also benefit engagement of non-scholarly end users, concluding with an invitation to the interdisciplinary research community to actively contribute to the given use case.
This research is focused on technology in the arts, social media, and the opera singer. Topics include recent performance trends, social media, marketing techniques, and creating a successful brand. This paper also focuses on how to leverage social media platforms, build a digital persona, and create an engaged audience. The same techniques used by corporations and opera companies for their social media and marketing strategy can be leveraged to increase brand awareness, build a strong network, and may aid in generating new opportunities for the opera singer.
Key Words: Social Media, Opera Singer, Branding, Marketing, Technology
Music recommender systems are lately seeing a sharp increase in popularity due to many novel commercial music streaming services. Most systems, however, do not decently take their listeners into account when recommending music items. In this note, we summarize our recent work and report our latest findings on the topics of tailoring music recommendations to individual listeners and to groups of listeners sharing certain characteristics. We focus on two tasks: context-aware automatic playlist generation (also known as serial recommendation) using sensor data and music artist recommendation using social media data.
Microblogs and Social Media applications are continuously growing in spread and importance. Users of Twitter, the currently most popular platform for microblogging, cre-ate more than a billion posts (called tweets) every week. Among all the different types of information being shared, some people post their music listening behavior, which is why Twitter became interesting for the Music Informa-tion Retrieval (MIR) community. Depending on the device and personal settings, some users provide geographic co-ordinates for their microposts. Having continuously crawled and analyzed tweets for more than 500 days (17 months) we can now present the "Million Musical Tweet Dataset" (MMTD) – the biggest publicly available source of microblog-based music listen-ing histories that includes geographic, temporal, and other contextual information. These extended information makes the MMTD outstanding from other datasets providing mu-sic listening histories. We introduce the dataset, give basic statistics about its composition, and show how this dataset allows to detect new contextual music listening patterns by performing a comprehensive statistical investigation with respect to cor-relation between music taste and day of the week, hour of day, and country.
This paper aims at leveraging microblogs to address two challenges in music information retrieval (MIR), similarity estimation between music artists and inferring typical listening patterns at different granularity levels (city, country, global). From two collections of several million microblogs, which we gathered over ten months, music-related information is extracted and statistically analyzed. We propose and evaluate four co-occurrence-based methods to compute artist similarity scores. Moreover, we derive and analyze culture-specific music listening patterns to investigate the diversity of listening behavior around the world.
Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity,
due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter
presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main
benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses
the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical
information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large
commercial recommender systems, are discussed, and a few solutions to overcome these problems are presented.
Music is a widely enjoyed content type, existing in many multifaceted representations. With the digital information age, a lot of digitized music information has theoretically become available at the user's fingertips. However, the abundance of information is too large-scaled and too diverse to annotate, oversee and present in a consistent and human manner, motivating the development of automated Music Information Retrieval (Music-IR) techniques.
In this paper, we encourage to consider music content beyond a monomodal audio signal and argue that Music-IR approaches with multimodal and user-centered strategies are necessary to serve real-life usage patterns and maintain and improve accessibility of digital music data. After discussing relevant existing work in these directions, we show that the field of Music-IR faces similar challenges as neighboring fields, and thus suggest opportunities for joint collaboration and mutual inspiration.
This paper is a preliminary report of findings in an ongoing study of the role of music in the lives of homeless young people which is taking place in Vancouver, British Columbia and Seattle, WA. One hundred homeless young people in Vancouver took part in online surveys, 20 of these young people participated in interviews and 64 completed design activities. Surveys included demographic and music questions. Interviews consisted of questions about music listening and preferences. In the design activities, participants envisioned a music device and provided a drawing and a scenario. Since the study is on-going, findings are limited to descriptive analysis of survey data supplemented with interview data. These findings provide initial insights into music listening behaviors, social aspects of shared music interests, and preferred music genres, bands and artists, and moods.
The advantage of Factorization Machines over other factorization models is their ability to easily integrate and efficiently exploit auxiliary information to improve Collaborative Filtering. Until now, this auxiliary information has been drawn from external knowledge sources beyond the user-item matrix. In this paper, we demonstrate that Factorization Machines can exploit additional representations of information inherent in the user-item matrix to improve recommendation performance. We refer to our approach as 'Free Lunch' enhancement since it leverages clusters that are based on information that is present in the user-item matrix, but not otherwise directly exploited during matrix factorization. Borrowing clustering concepts from codebook sharing, our approach can also make use of 'Free Lunch' information inherent in a user-item matrix from an auxiliary domain that is different from the target domain of the recommender. Our approach improves performance both in the joint case, in which the auxiliary and target domains share users, and in the disjoint case, in which they do not. Although the 'Free Lunch' enhancement does not apply equally well to any given domain or domain combination, our overall conclusion is that Factorization Machines present an opportunity to exploit information that is ubiquitously present, but commonly under-appreciated by Collaborative Filtering algorithms.
Factorization machines offer an advantage over other existing collaborative filtering approaches to recommendation. They make it possible to work with any auxiliary information that can be encoded as a real-valued feature vector as a supplement to the information in the user-item matrix. We build on the assumption that different patterns characterize the way that users interact with (i.e., rate or download) items of a certain type (e.g., movies or books). We view interactions with a specific type of item as constituting a particular domain and allow interaction information from an auxiliary domain to inform recommendation in a target domain. Our proposed approach is tested on a data set from Amazon and compared with a state-of-the-art approach that has been proposed for Cross-Domain Collaborative Filtering. Experimental results demonstrate that our approach, which has a lower computational complexity, is able to achieve performance improvements.
We present results of text data mining experiments for music retrieval, analyzing microblogs gathered from November 2011 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.
The fast growth of online communities and increasing popularity of internet-accessing smart devices have significantly changed the way people consume and share music. As an emerging technology to facilitate effective music retrieval on the move, intelligent recommendation has been recently received great attentions in recent years. While a large amount of efforts have been invested in the field, the technology is still in its infancy. One of the major reasons for this stagnation is due to inability of the existing approaches to comprehensively take multiple kinds of contextual information into account. In the paper, we present a novel recommender system called Just-for-Me to facilitate effective social music recommendation by considering users' location related contexts as well as global music popularity trends. We also develop an unified recommendation model to integrate the contextual factors as well as music contents simultaneously. Furthermore, pseudo-observations are proposed to overcome the cold-start and sparsity problems. An extensive experimental study based on different test collections demonstrates that Just-for-Me system can significantly improve the recommendation performance at various geo-locations.
Teenagers, Internet and Black Metal
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Cross-Domain Collaborative Filtering with Factorization Machines
- B Loni
- Y Shi
- M Larson
- A Hanjalic
Using Social Media Mining for Estimating Theory of Planned Behaviour Parameters
- M Tkalvcivc
- B Ferwerda
- M Schedl
- C Liem
- M Melenhorst
- A Odić
- A Kosir
Exploiting Twitter's Collective Knowledge for Music Recommendations
- E Zangerle
- W Gassler
- G Specht