{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:48:38Z","timestamp":1780609718371,"version":"3.54.1"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100008332","name":"Technische Universit\u00e4t Graz","doi-asserted-by":"publisher","award":["Open Access Publishing Fund"],"award-info":[{"award-number":["Open Access Publishing Fund"]}],"id":[{"id":"10.13039\/100008332","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["V579"],"award-info":[{"award-number":["V579"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EPJ Data Sci."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-021-00268-9","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T23:03:53Z","timestamp":1617059033000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Support the underground: characteristics of beyond-mainstream music listeners"],"prefix":"10.1140","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3230-6234","authenticated-orcid":false,"given":"Dominik","family":"Kowald","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-1945","authenticated-orcid":false,"given":"Peter","family":"Muellner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3195-8273","authenticated-orcid":false,"given":"Eva","family":"Zangerle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5724-1137","authenticated-orcid":false,"given":"Christine","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1706-3406","authenticated-orcid":false,"given":"Markus","family":"Schedl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5293-2967","authenticated-orcid":false,"given":"Elisabeth","family":"Lex","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"268_CR1","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/978-1-4899-7637-6_13","volume-title":"Recommender systems handbook","author":"M Schedl","year":"2015","unstructured":"Schedl M, Knees P, McFee B, Bogdanov D, Kaminskas M (2015) Music recommender systems. In: Recommender systems handbook, pp\u00a0453\u2013492"},{"key":"268_CR2","volume-title":"RMSE workshop held in conjunction with the 13th ACM conferenceon recommender systems (RecSys)","author":"H Abdollahpouri","year":"2019","unstructured":"Abdollahpouri H, Mansoury M, Burke R, Mobasher B (2019) The unfairness of popularity bias in recommendation. In: RMSE workshop held in conjunction with the 13th ACM conferenceon recommender systems (RecSys)"},{"key":"268_CR3","doi-asserted-by":"crossref","unstructured":"Celma O (2009) Music recommendation and discovery in the long tail. PhD thesis, Universitat Pompeu Fabra","DOI":"10.1007\/978-3-642-13287-2"},{"key":"268_CR4","first-page":"35","volume-title":"European conference on information retrieval","author":"D Kowald","year":"2020","unstructured":"Kowald D, Schedl M, Lex E (2020) The unfairness of popularity bias in music recommendation: a reproducibility study. In: European conference on information retrieval. Springer, Berlin, pp\u00a035\u201342"},{"key":"268_CR5","volume-title":"Proceedings of KDD \u20192018 (Netflix price workshop)","author":"O Celma","year":"2008","unstructured":"Celma O, Cano P (2008) From hits to niches?: or how popular artists can bias music recommendation and discovery. In: Proceedings of KDD \u20192018 (Netflix price workshop)"},{"key":"268_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13287-2","volume-title":"Music recommendation and discovery\u2014the long tail, long fail, and long play in the digital music space","author":"O Celma","year":"2010","unstructured":"Celma O (2010) Music recommendation and discovery\u2014the long tail, long fail, and long play in the digital music space. Springer"},{"key":"268_CR7","first-page":"2643","volume-title":"Proceedings of NIPS \u20192013","author":"A van den Oord","year":"2013","unstructured":"van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Proceedings of NIPS \u20192013. Curran Associates, Red Hook, pp\u00a02643\u20132651"},{"key":"268_CR8","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1145\/1718487.1718513","volume-title":"Proceedings of the third ACM international conference on web search and data mining","author":"S Goel","year":"2010","unstructured":"Goel S, Broder A, Gabrilovich E, Pang B (2010) Anatomy of the long tail: ordinary people with extraordinary tastes. In: Proceedings of the third ACM international conference on web search and data mining, pp\u00a0201\u2013210"},{"key":"268_CR9","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/978-3-642-38844-6_16","volume-title":"User modeling, adaptation, and personalization","author":"N Tintarev","year":"2013","unstructured":"Tintarev N, Dennis M, Masthoff J (2013) Adapting recommendation diversity to openness to experience: a study of human behaviour. In: Carberry S, Weibelzahl S, Micarelli A, Semeraro G (eds) User modeling, adaptation, and personalization. Springer, Berlin, pp\u00a0190\u2013202"},{"issue":"2","key":"268_CR10","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TII.2014.2308433","volume":"10","author":"X Luo","year":"2014","unstructured":"Luo X, Zhou M, Xia Y, Zhu Q (2014) An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans Ind Inform 10(2):1273\u20131284","journal-title":"IEEE Trans Ind Inform"},{"issue":"1","key":"268_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/963770.963772","volume":"22","author":"JL Herlocker","year":"2004","unstructured":"Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5\u201353","journal-title":"ACM Trans Inf Syst"},{"issue":"2","key":"268_CR12","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s13735-018-0154-2","volume":"7","author":"M Schedl","year":"2018","unstructured":"Schedl M, Zamani H, Chen C-W, Deldjoo Y, Elahi M (2018) Current challenges and visions in music recommender systems research. Int J Multimed Inf Retr 7(2):95\u2013116","journal-title":"Int J Multimed Inf Retr"},{"key":"268_CR13","volume-title":"Music that works: contributions of biology, neurophysiology, psychology, sociology, medicine and musicology","author":"R Haas","year":"2010","unstructured":"Haas R, Brandes V (2010) Music that works: contributions of biology, neurophysiology, psychology, sociology, medicine and musicology. Springer Science & Business Media"},{"key":"268_CR14","volume-title":"Introduction to the sociology of music","author":"TW Adorno","year":"1988","unstructured":"Adorno TW (1988) Introduction to the sociology of music. Burns & Oates"},{"key":"268_CR15","volume-title":"Psychology of music","author":"D Deutsch","year":"2013","unstructured":"Deutsch D (2013) Psychology of music. Elsevier"},{"key":"268_CR16","volume-title":"Proceedings of the International Society for Music Information Retrieval conference (ISMIR)","author":"A Laplante","year":"2014","unstructured":"Laplante A (2014) Improving music recommender systems: what can we learn from research on music tastes? In: Proceedings of the International Society for Music Information Retrieval conference (ISMIR)"},{"issue":"2","key":"268_CR17","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1177\/0305735607070382","volume":"35","author":"PJ Rentfrow","year":"2007","unstructured":"Rentfrow PJ, Gosling SD (2007) The content and validity of music-genre stereotypes among college students. Psychol Music 35(2):306\u2013326","journal-title":"Psychol Music"},{"issue":"1","key":"268_CR18","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-020-0221-9","volume":"9","author":"Y Kim","year":"2020","unstructured":"Kim Y, Aiello LM, Quercia D (2020) Pepmusic: motivational qualities of songs for daily activities. EPJ Data Sci 9(1):13","journal-title":"EPJ Data Sci"},{"key":"268_CR19","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780192631886.001.0001","volume-title":"Music and emotion: theory and research","author":"PN Juslin","year":"2001","unstructured":"Juslin PN, Sloboda JA (2001) Music and emotion: theory and research. Oxford University Press"},{"issue":"4","key":"268_CR20","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1037\/1528-3542.8.4.494","volume":"8","author":"M Zentner","year":"2008","unstructured":"Zentner M, Grandjean D, Scherer KR (2008) Emotions evoked by the sound of music: characterization, classification, and measurement. Emotion 8(4):494","journal-title":"Emotion"},{"issue":"3","key":"268_CR21","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1080\/0929821042000317813","volume":"33","author":"PN Juslin","year":"2004","unstructured":"Juslin PN, Laukka P (2004) Expression, perception, and induction of musical emotions: a review and a questionnaire study of everyday listening. J\u00a0New Music Res 33(3):217\u2013238","journal-title":"J\u00a0New Music Res"},{"key":"268_CR22","doi-asserted-by":"publisher","DOI":"10.1201\/b10731","volume-title":"Music emotion recognition","author":"Y-H Yang","year":"2011","unstructured":"Yang Y-H, Chen HH (2011) Music emotion recognition. CRC Press"},{"key":"268_CR23","volume-title":"Late-breaking results of 23rd international conference on user modeling, adaptation and personalization (UMAP)","author":"B Ferwerda","year":"2015","unstructured":"Ferwerda B, Schedl M, Tkalcic M (2015) Personality & emotional states: understanding users\u2019 music listening needs. In: Late-breaking results of 23rd international conference on user modeling, adaptation and personalization (UMAP)"},{"issue":"1","key":"268_CR24","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1037\/0003-066X.48.1.26","volume":"48","author":"LR Goldberg","year":"1993","unstructured":"Goldberg LR (1993) The structure of phenotypic personality traits. Am Psychol 48(1):26","journal-title":"Am Psychol"},{"issue":"3","key":"268_CR25","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1177\/0305735607072657","volume":"35","author":"E Schubert","year":"2007","unstructured":"Schubert E (2007) The influence of emotion, locus of emotion and familiarity upon preference in music. Psychol Music 35(3):499\u2013515","journal-title":"Psychol Music"},{"issue":"11","key":"268_CR26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0027241","volume":"6","author":"CS Pereira","year":"2011","unstructured":"Pereira CS, Teixeira J, Figueiredo P, Xavier J, Castro SL, Brattico E (2011) Music and emotions in the brain: familiarity matters. PLoS ONE 6(11):e27241","journal-title":"PLoS ONE"},{"key":"268_CR27","first-page":"401","volume-title":"Proceedings of the International Society for Music Information Retrieval conference (ISMIR)","author":"JL Moore","year":"2013","unstructured":"Moore JL, Chen S, Turnbull D, Joachims T (2013) Taste over time: the temporal dynamics of user preferences. In: Proceedings of the International Society for Music Information Retrieval conference (ISMIR), pp\u00a0401\u2013406"},{"key":"268_CR28","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2017.00931","volume":"8","author":"MD Barone","year":"2017","unstructured":"Barone MD, Bansal J, Woolhouse MH (2017) Acoustic features influence musical choices across multiple genres. Front Psychol 8:931","journal-title":"Front Psychol"},{"key":"268_CR29","unstructured":"Gong B, Kaya M, Tintarev N (2020) Contextual personalized re-ranking of music recommendations through audio features. Master\u2019s thesis, TU Delft"},{"key":"268_CR30","first-page":"709","volume-title":"Proceedings of the 19th International Society for Music Information Retrieval conference 2018 (ISMIR 2018)","author":"E Zangerle","year":"2018","unstructured":"Zangerle E, Pichl M (2018) Content-based user models: modeling the many faces of musical preference. In: Proceedings of the 19th International Society for Music Information Retrieval conference 2018 (ISMIR 2018), pp\u00a0709\u2013716"},{"key":"268_CR31","first-page":"172","volume-title":"Conference on fairness, accountability and transparency","author":"MD Ekstrand","year":"2018","unstructured":"Ekstrand MD, Tian M, Azpiazu IM, Ekstrand JD, Anuyah O, McNeill D, Pera MS (2018) All the cool kids, how do they fit in?: popularity and demographic biases in recommender evaluation and effectiveness. In: Conference on fairness, accountability and transparency, pp\u00a0172\u2013186"},{"issue":"4","key":"268_CR32","first-page":"67","volume":"47","author":"E Brynjolfsson","year":"2006","unstructured":"Brynjolfsson E, Hu YJ, Smith MD (2006) From niches to riches: anatomy of the long tail. Sloan Manag Rev 47(4):67\u201371","journal-title":"Sloan Manag Rev"},{"issue":"5","key":"268_CR33","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s11257-015-9165-3","volume":"25","author":"D Jannach","year":"2015","unstructured":"Jannach D, Lerche L, Kamehkhosh I, Jugovac M (2015) What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Model User-Adapt Interact 25(5):427\u2013491","journal-title":"User Model User-Adapt Interact"},{"issue":"4","key":"268_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2827872","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper FM, Konstan JA (2015) The movielens datasets: history and context. ACM Trans Interact Intell Syst 5(4):1\u201319","journal-title":"ACM Trans Interact Intell Syst"},{"key":"268_CR35","first-page":"203","volume-title":"Pacific-Asia conference on knowledge discovery and data mining","author":"R Cheng","year":"2016","unstructured":"Cheng R, Tang B (2016) A\u00a0music recommendation system based on acoustic features and user personalities. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp\u00a0203\u2013213"},{"key":"268_CR36","first-page":"17","volume-title":"Proceedings of RecSys \u20192013","author":"M Kaminskas","year":"2013","unstructured":"Kaminskas M, Ricci F, Schedl M (2013) Location-aware music recommendation using auto-tagging and hybrid matching. In: Proceedings of RecSys \u20192013. ACM, Hong Kong, pp\u00a017\u201324"},{"key":"268_CR37","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1145\/1297231.1297271","volume-title":"Proceedings of RecSys \u20192007","author":"J Donaldson","year":"2007","unstructured":"Donaldson J (2007) A\u00a0hybrid social-acoustic recommendation system for popular music. In: Proceedings of RecSys \u20192007. ACM, New York, pp\u00a0187\u2013190"},{"key":"268_CR38","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/978-3-319-29659-3_6","volume-title":"Recommender systems","author":"CC Aggarwal","year":"2016","unstructured":"Aggarwal CC (2016) Ensemble-based and hybrid recommender systems. In: Recommender systems, pp\u00a0199\u2013224"},{"key":"268_CR39","volume-title":"19th International Society for Music Information Retrieval conference (ISMIR)","author":"E Zangerle","year":"2018","unstructured":"Zangerle E, Pichl M (2018) Content-based user models: modeling the many faces of musical preference. In: 19th International Society for Music Information Retrieval conference (ISMIR)"},{"key":"268_CR40","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1145\/2043932.2043971","volume-title":"Proceedings of the 5th ACM conference on recommender systems","author":"K Lee","year":"2011","unstructured":"Lee K, Lee K (2011) My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation. In: Proceedings of the 5th ACM conference on recommender systems, pp\u00a0213\u2013220"},{"issue":"1","key":"268_CR41","first-page":"17","volume":"3","author":"E Lex","year":"2020","unstructured":"Lex E, Kowald D, Schedl M (2020) Modeling popularity and temporal drift of music genre preferences. Trans Int Soc Music Inf Retr 3(1):17\u201330","journal-title":"Trans Int Soc Music Inf Retr"},{"key":"268_CR42","volume-title":"Late-breaking-results of the 20th annual conference of the International Society for Music Information Retrieval (ISMIR)","author":"D Kowald","year":"2019","unstructured":"Kowald D, Lex E, Schedl M (2019) Modeling artist preferences of users with different music consumption patterns for fair music recommendations. In: Late-breaking-results of the 20th annual conference of the International Society for Music Information Retrieval (ISMIR)"},{"key":"268_CR43","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1145\/3099023.3099069","volume-title":"Adjunct publication of the 25th conference on user modeling, adaptation and personalization","author":"D Kowald","year":"2017","unstructured":"Kowald D, Kopeinik S, Lex E (2017) The tagrec framework as a toolkit for the development of tag-based recommender systems. In: Adjunct publication of the 25th conference on user modeling, adaptation and personalization, pp\u00a023\u201328"},{"key":"268_CR44","volume-title":"1st workshop on Designing Human-Centric Music Information Research systems in conjunction with ISMIR confernce","author":"C Bauer","year":"2019","unstructured":"Bauer C (2019) Allowing for equal opportunities for artists in music recommendation. In: 1st workshop on Designing Human-Centric Music Information Research systems in conjunction with ISMIR confernce"},{"key":"268_CR45","first-page":"475","volume-title":"IEEE international symposium on multimedia, ISM 2016","author":"M Pichl","year":"2016","unstructured":"Pichl M, Zangerle E, Specht G (2016) Understanding playlist creation on music streaming platforms. In: IEEE international symposium on multimedia, ISM 2016, pp\u00a0475\u2013480"},{"key":"268_CR46","first-page":"1","volume-title":"4th international workshop on cognitive information processing (CIP)","author":"JS Andersen","year":"2014","unstructured":"Andersen JS (2014) Using the echo nest\u2019s automatically extracted music features for a musicological purpose. In: 4th international workshop on cognitive information processing (CIP), pp\u00a01\u20136"},{"key":"268_CR47","first-page":"783","volume-title":"Proceedings of the 11th International Society for Music Information Retrieval conference (ISMIR)","author":"M McVicar","year":"2011","unstructured":"McVicar M, Freeman T, De Bie T (2011) Mining the correlation between lyrical and audio features and the emergence of mood. In: Proceedings of the 11th International Society for Music Information Retrieval conference (ISMIR), pp\u00a0783\u2013788"},{"issue":"1","key":"268_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5334\/tismir.37","volume":"3","author":"E Zangerle","year":"2020","unstructured":"Zangerle E, Pichl M, Schedl M (2020) User models for culture-aware music recommendation: fusing acoustic and cultural cues. Trans Int Soc Music Inf Retr 3(1):1\u201316. https:\/\/doi.org\/10.5334\/tismir.37","journal-title":"Trans Int Soc Music Inf Retr"},{"key":"268_CR49","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/2911996.2912004","volume-title":"Proceedings of the 2016 ACM on international conference on multimedia retrieval","author":"M Schedl","year":"2016","unstructured":"Schedl M (2016) The lfm-1b dataset for music retrieval and recommendation. In: Proceedings of the 2016 ACM on international conference on multimedia retrieval. ACM, New York, pp\u00a0103\u2013110"},{"key":"268_CR50","doi-asserted-by":"publisher","unstructured":"Zangerle E Culture-aware music recommendation dataset. https:\/\/doi.org\/10.5281\/zenodo.3477842","DOI":"10.5281\/zenodo.3477842"},{"issue":"6","key":"268_CR51","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0217389","volume":"14","author":"C Bauer","year":"2019","unstructured":"Bauer C, Schedl M (2019) Global and country-specific mainstreaminess measures: definitions, analysis, and usage for improving personalized music recommendation systems. PLoS ONE 14(6):e0217389. https:\/\/doi.org\/10.1371\/journal.pone.0217389","journal-title":"PLoS ONE"},{"issue":"1\/2","key":"268_CR52","doi-asserted-by":"publisher","first-page":"81","DOI":"10.2307\/2332226","volume":"30","author":"MG Kendall","year":"1938","unstructured":"Kendall MG (1938) A\u00a0new measure of rank correlation. Biometrika 30(1\/2):81\u201393","journal-title":"Biometrika"},{"issue":"4","key":"268_CR53","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1214\/088342304000000297","volume":"19","author":"SJ Sheather","year":"2004","unstructured":"Sheather SJ (2004) Density estimation. Stat Sci 19(4):588\u2013597","journal-title":"Stat Sci"},{"key":"268_CR54","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-1-4419-8339-8_13","volume-title":"Selected works of Murray Rosenblatt","author":"RA Davis","year":"2011","unstructured":"Davis RA, Lii K-S, Politis DN (2011) Remarks on some nonparametric estimates of a density function. In: Selected works of Murray Rosenblatt. Springer, New York, pp\u00a095\u2013100"},{"key":"268_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/b98885","volume-title":"Numerical mathematics","author":"A Quarteroni","year":"2007","unstructured":"Quarteroni A, Sacco R, Saleri F (2007) Numerical mathematics. Springer, New York"},{"issue":"1","key":"268_CR56","first-page":"11","volume":"28","author":"KS Jones","year":"1972","unstructured":"Jones KS (1972) A\u00a0statistical interpretation of term specificity and its application in retrieval. J\u00a0Doc 28(1):11\u201321","journal-title":"J\u00a0Doc"},{"key":"268_CR57","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1145\/3099023.3099098","volume-title":"Adjunct publication of the 25th conference on user modeling, adaptation and personalization","author":"M Schedl","year":"2017","unstructured":"Schedl M, Bauer C (2017) Distance-and rank-based music mainstreaminess measurement. In: Adjunct publication of the 25th conference on user modeling, adaptation and personalization. ACM, New York, pp\u00a0364\u2013367"},{"issue":"1","key":"268_CR58","doi-asserted-by":"publisher","DOI":"10.1145\/1644873.1644874","volume":"4","author":"Y Koren","year":"2010","unstructured":"Koren Y (2010) Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data 4(1):1","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"1","key":"268_CR59","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (mae) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30(1):79\u201382","journal-title":"Clim Res"},{"key":"268_CR60","first-page":"349","volume-title":"Proceedings of the 12th International Society for Music Information Retrieval conference (ISMIR)","author":"JL Moore","year":"2012","unstructured":"Moore JL, Chen S, Joachims T, Turnbull D (2012) Learning to embed songs and tags for playlist prediction. In: Proceedings of the 12th International Society for Music Information Retrieval conference (ISMIR), vol\u00a012, pp\u00a0349\u2013354"},{"issue":"2","key":"268_CR61","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1080\/09298210802479292","volume":"37","author":"M Levy","year":"2008","unstructured":"Levy M, Sandler M (2008) Learning latent semantic models for music from social tags. J\u00a0New Music Res 37(2):137\u2013150","journal-title":"J\u00a0New Music Res"},{"issue":"2","key":"268_CR62","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1162\/089976699300016728","volume":"11","author":"ME Tipping","year":"1999","unstructured":"Tipping ME, Bishop CM (1999) Mixtures of probabilistic principal component analyzers. Neural Comput 11(2):443\u2013482","journal-title":"Neural Comput"},{"issue":"5500","key":"268_CR63","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"issue":"2","key":"268_CR64","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02289694","volume":"29","author":"JB Kruskal","year":"1964","unstructured":"Kruskal JB (1964) Nonmetric multidimensional scaling: a numerical method. Psychometrika 29(2):115\u2013129","journal-title":"Psychometrika"},{"issue":"5500","key":"268_CR65","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum JB, De Silva V, Langford JC (2000) A\u00a0global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319\u20132323","journal-title":"Science"},{"key":"268_CR66","first-page":"849","volume-title":"Advances in neural information processing systems","author":"AY Ng","year":"2002","unstructured":"Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. In: Advances in neural information processing systems, pp\u00a0849\u2013856"},{"key":"268_CR67","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J\u00a0Mach Learn Res 9:2579\u20132605","journal-title":"J\u00a0Mach Learn Res"},{"key":"268_CR68","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J (2018) UMAP: uniform manifold approximation and projection for dimension reduction. J\u00a0Open Sour Softw","DOI":"10.21105\/joss.00861"},{"issue":"29","key":"268_CR69","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes L, Healy J, Saul N, Grossberger L (2018) UMAP: uniform manifold approximation and projection. J\u00a0Open Sour Softw 3(29):861","journal-title":"J\u00a0Open Sour Softw"},{"key":"268_CR70","first-page":"226","volume-title":"KDD","author":"M Ester","year":"1996","unstructured":"Ester M, Kriegel H-P, Sander J, Xu X et al. (1996) A\u00a0density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol\u00a096, pp\u00a0226\u2013231"},{"key":"268_CR71","first-page":"424","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning Springer, New York, pp\u00a0424\u2013429"},{"key":"268_CR72","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1007\/978-1-4899-7488-4_196","volume-title":"Encyclopedia of biometrics","author":"D Reynolds","year":"2015","unstructured":"Reynolds D (2015) Gaussian mixture models. In: Encyclopedia of biometrics, pp\u00a0827\u2013832"},{"issue":"5814","key":"268_CR73","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972\u2013976","journal-title":"Science"},{"key":"268_CR74","unstructured":"Shi J, Malik J (2000) Normalized cuts and image segmentation. Departmental Papers (CIS), 107"},{"issue":"3","key":"268_CR75","first-page":"274","volume":"31","author":"F Murtagh","year":"2014","unstructured":"Murtagh F, Legendre P (2014) Ward\u2019s hierarchical agglomerative clustering method: which algorithms implement Ward\u2019s criterion? J\u00a0Classif 31(3):274\u2013295","journal-title":"J\u00a0Classif"},{"key":"268_CR76","first-page":"49","volume-title":"ACM sigmod record","author":"M Ankerst","year":"1999","unstructured":"Ankerst M, Breunig MM, Kriegel H-P, Sander J (1999) Optics: ordering points to identify the clustering structure. In: ACM sigmod record, vol\u00a028. ACM, New York, pp\u00a049\u201360"},{"key":"268_CR77","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/ICDMW.2017.12","volume-title":"Data mining workshops (ICDMW), 2017 IEEE international conference on","author":"L McInnes","year":"2017","unstructured":"McInnes L, Healy J (2017) Accelerated hierarchical density based clustering. In: Data mining workshops (ICDMW), 2017 IEEE international conference on. IEEE, New York, pp\u00a033\u201342"},{"key":"268_CR78","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1145\/3099023.3099075","volume-title":"Adjunct publication of the 25th conference on user modeling, adaptation and personalization","author":"S Yoo","year":"2017","unstructured":"Yoo S, Lee K (2017) A\u00a0data-driven approach to identifying music listener groups based on users\u2019 playrate distributions of listening events. In: Adjunct publication of the 25th conference on user modeling, adaptation and personalization, pp\u00a077\u201381"},{"issue":"11","key":"268_CR79","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes L, Healy J, Astels S (2017) hdbscan: hierarchical density based clustering. J\u00a0Open Sour Softw 2(11):205","journal-title":"J\u00a0Open Sour Softw"},{"key":"268_CR80","unstructured":"York W (2004) Voices from hell\u2014the dark, not-so-dulcet cookie monster vocals of extreme metal. The San Francisco Bay Guardian, 14\u201320"},{"issue":"1","key":"268_CR81","doi-asserted-by":"publisher","DOI":"10.1186\/s40649-017-0045-3","volume":"4","author":"D Lamprecht","year":"2017","unstructured":"Lamprecht D, Strohmaier M, Helic D (2017) A\u00a0method for evaluating discoverability and navigability of recommendation algorithms. Comput Soc Netw 4(1):9","journal-title":"Comput Soc Netw"},{"key":"268_CR82","first-page":"101","volume-title":"Proceedings of the 13th ACM conference on recommender systems, RecSys 2019","author":"MF Dacrema","year":"2019","unstructured":"Dacrema MF, Cremonesi P, Jannach D (2019) Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In: Proceedings of the 13th ACM conference on recommender systems, RecSys 2019, Copenhagen, Denmark, September 16\u201320, 2019, pp\u00a0101\u2013109"},{"key":"268_CR83","volume-title":"Cultures and organizations: software of the mind","author":"G Hofstede","year":"2010","unstructured":"Hofstede G, Hofstede GJ, Minkov M (2010) Cultures and organizations: software of the mind, 3rd edn. McGraw-Hill, New York","edition":"3"},{"key":"268_CR84","volume-title":"World happiness report 2016 update","author":"JF Helliwell","year":"2016","unstructured":"Helliwell JF, Layard R, Sachs J (2016) World happiness report 2016 update. Sustainable Development Solutions Network, New York"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-021-00268-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-021-00268-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-021-00268-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T02:41:31Z","timestamp":1724726491000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-021-00268-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,30]]},"references-count":84,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["268"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-021-00268-9","relation":{},"ISSN":["2193-1127"],"issn-type":[{"value":"2193-1127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,30]]},"assertion":[{"value":"7 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14"}}