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Schedl, \u201cA survey of music similarity and recommendation from music context data,\u201d ACM Transactions on Multimedia Computing, Communications, and Applications, vol.10, no.1, pp.1-21, 2013. 10.1145\/2542205.2542206","DOI":"10.1145\/2542205.2542206"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] Y. Deldjoo, M. Schedl, P. Cremonesi, and G. Pasi, \u201cRecommender systems leveraging multimedia content,\u201d ACM Computing Surveys, vol.53, no.5, pp.1-38, 2020. 10.1145\/3407190","DOI":"10.1145\/3407190"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] J.D. Boyle, G.L. Hosterman, and D.S. Ramsey, \u201cFactors influencing pop music preferences of young people,\u201d Journal of Research in Music Education, vol.29, no.1, pp.47-55, 1981. 10.2307\/3344679","DOI":"10.2307\/3344679"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] A. Greasley, A. Lamont, and J. Sloboda, \u201cExploring musical preferences: An in-depth qualitative study of adults\u2019 liking for music in their personal collections,\u201d Qualitative Research in Psychology, vol.10, no.4, pp.402-427, 2013. 10.1080\/14780887.2011.647259","DOI":"10.1080\/14780887.2011.647259"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] K.R.M. Sanfilippo, N. Spiro, M. Molina-Solana, and A. Lamont, \u201cDo the shuffle: Exploring reasons for music listening through shuffled play,\u201d PLOS ONE, vol.15, no.2, e0228457, pp.1-21, 2020. 10.1371\/journal.pone.0228457","DOI":"10.1371\/journal.pone.0228457"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] B. Ferwerda, M. Tkalcic, and M. Schedl, \u201cPersonality traits and music genres: What do people prefer to listen to?,\u201d Proc. 25th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2017, pp.285-288, 2017. 10.1145\/3079628.3079693","DOI":"10.1145\/3079628.3079693"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] Y. Jin, N. Tintarev, and K. Verbert, \u201cEffects of personal characteristics on music recommender systems with different levels of controllability,\u201d Proc. 12th ACM Conference on Recommender Systems, RecSys 2018, pp.13-21, 2018. 10.1145\/3240323.3240358","DOI":"10.1145\/3240323.3240358"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] T. Chamorro-Premuzic and A. Furnham, \u201cPersonality and music: Can traits explain how people use music in everyday life?,\u201d British Journal of Psychology, vol.98, no.2, pp.175-185, 2007. 10.1348\/000712606x111177","DOI":"10.1348\/000712606X111177"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] M.J.M.H. Delsing, T.F.M. ter Bogt, R.C.M.E. Engels, and W.H.J. Meeus, \u201cAdolescents\u2019 music preferences and personality characteristics,\u201d European Journal of Personality, vol.22, no.2, pp.109-130, 2008. 10.1002\/per.665","DOI":"10.1002\/per.665"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] R.L. Zweigenhaft, \u201cA do re mi encore: A closer look at the personality correlates of music preferences,\u201d Journal of Individual Differences, vol.29, no.1, pp.45-55, 2008. 10.1027\/1614-0001.29.1.45","DOI":"10.1027\/1614-0001.29.1.45"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] R.A. Brown, \u201cMusic preferences and personality among Japanese university students,\u201d International Journal of Psychology, vol.47, no.4, pp.259-268, 2012. 10.1080\/00207594.2011.631544","DOI":"10.1080\/00207594.2011.631544"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] A. Langmeyer, A. Guglh\u00f6r-Rudan, and C. Tarnai, \u201cWhat do music preferences reveal about personality?,\u201d Journal of Individual Differences, vol.33, no.2, pp.119-130, 2012. 10.1027\/1614-0001\/a000082","DOI":"10.1027\/1614-0001\/a000082"},{"key":"16","unstructured":"[16] A. Laplante, \u201cImproving music recommender systems: What can we learn from research on music tastes?,\u201d Proc. 15th International Society for Music Information Retrieval Conference, ISMIR 2014, pp.451-456, 2014. 10.5281\/zenodo.1417797"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] T. Chamorro-Premuzic, V. Swami, and B. Cermakova, \u201cIndividual differences in music consumption are predicted by uses of music and age rather than emotional intelligence, neuroticism, extraversion or openness,\u201d Psychology of Music, vol.40, no.3, pp.285-300, 2012. 10.1177\/0305735610381591","DOI":"10.1177\/0305735610381591"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] T. Sch\u00e4fer and C. Mehlhorn, \u201cCan personality traits predict musical style preferences? A meta-analysis,\u201d Personality and Individual Differences, vol.116, pp.265-273, 2017. 10.1016\/j.paid.2017.04.061","DOI":"10.1016\/j.paid.2017.04.061"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] Y. Liang and M.C. 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Jones, S.J. Cunningham, and M. Jones, \u201cOrganizing digital music for use: An examination of personal music collections,\u201d Proc. 5th International Conference on Music Information Retrieval, ISMIR 2004, pp.397-402, 2004. 10.5281\/zenodo.1416298"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] C. Inskip, R. Butterworth, and A. MacFarlane, \u201cA study of the information needs of the users of a folk music library and the implications for the design of a digital library system,\u201d Information Processing &amp; Management, vol.44, no.2, pp.647-662, 2008. 10.1016\/j.ipm.2007.05.004","DOI":"10.1016\/j.ipm.2007.05.004"},{"key":"30","unstructured":"[30] X. Hu, J.H. Lee, and L.K.Y. 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Kaneshiro, \u201cSocial music curation that works: Insights from successful collaborative playlists,\u201d Proc. ACM on Human-Computer Interaction, vol.5, no.CSCW1, pp.1-27, 2021. 10.1145\/3449191","DOI":"10.1145\/3449191"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] J.A. Sloboda, S.A. O\u2019Neill, and A. Ivaldi, \u201cFunctions of music in everyday life: An exploratory study using the experience sampling method,\u201d Musicae Scientiae, vol.5, no.1, pp.9-32, 2001. 10.1177\/102986490100500102","DOI":"10.1177\/102986490100500102"},{"key":"38","doi-asserted-by":"publisher","unstructured":"[38] A. Lamont and R. Webb, \u201cShort- and long-term musical preferences: What makes a favourite piece of music?,\u201d Psychology of Music, vol.38, no.2, pp.222-241, 2010. 10.1177\/0305735609339471","DOI":"10.1177\/0305735609339471"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[39] A.B. Haake, \u201cIndividual music listening in workplace settings: An exploratory survey of offices in the UK,\u201d Musicae Scientiae, vol.15, no.1, pp.107-129, 2011. 10.1177\/1029864911398065","DOI":"10.1177\/1029864911398065"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[40] T. Sch\u00e4fer, \u201cThe goals and effects of music listening and their relationship to the strength of music preference,\u201d PLOS ONE, vol.11, no.3, e0151634, pp.1-15, 2016. 10.1371\/journal.pone.0151634","DOI":"10.1371\/journal.pone.0151634"},{"key":"41","doi-asserted-by":"publisher","unstructured":"[41] A.J. Lonsdale and A.C. North, \u201cWhy do we listen to music? A uses and gratifications analysis,\u201d British Journal of Psychology, vol.102, no.1, pp.108-134, 2011. 10.1348\/000712610x506831","DOI":"10.1348\/000712610X506831"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] S.Y. Park, E. Redmond, J. Berger, and B. Kaneshiro, \u201cHitting pause: How user perceptions of collaborative playlists evolved in the united states during the COVID-19 pandemic,\u201d Proc. 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022, pp.1-16, 2022. 10.1145\/3491102.3517604","DOI":"10.1145\/3491102.3517604"},{"key":"43","doi-asserted-by":"publisher","unstructured":"[43] A.C. North, D.J. Hargreaves, and J.J. Hargreaves, \u201cUses of music in everyday life,\u201d Music Perception: An Interdisciplinary Journal, vol.22, no.1, pp.41-77, 2004. 10.1525\/mp.2004.22.1.41","DOI":"10.1525\/mp.2004.22.1.41"},{"key":"44","doi-asserted-by":"publisher","unstructured":"[44] P.J. Rentfrow and S.D. Gosling, \u201cThe do re mi\u2019s of everyday life: The structure and personality correlates of music preferences.,\u201d Journal of Personality and Social Psychology, vol.84, no.6, pp.1236-1256, 2003. 10.1037\/0022-3514.84.6.1236","DOI":"10.1037\/0022-3514.84.6.1236"},{"key":"45","doi-asserted-by":"crossref","unstructured":"[45] M. Tkal\u010di\u010d, B. Ferwerda, D. Hauger, and M. Schedl, \u201cPersonality correlates for digital concert program notes,\u201d Proc. 23rd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2015, pp.364-369, 2015. 10.1007\/978-3-319-20267-9_32","DOI":"10.1007\/978-3-319-20267-9_32"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] A.B. Melchiorre and M. Schedl, \u201cPersonality correlates of music audio preferences for modelling music listeners,\u201d Proc. 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2020, pp.313-317, 2020. 10.1145\/3340631.3394874","DOI":"10.1145\/3340631.3394874"},{"key":"47","unstructured":"[47] B. Ferwerda, M. Graus, A. Vall, M. Tkalcic, and M. Schedl, \u201cThe influence of users\u2019 personality traits on satisfaction and attractiveness of diversified recommendation lists,\u201d Proc. 4th Workshop on Emotions and Personality in Personalized Systems, EMPIRE 2016, pp.43-47, 2016."},{"key":"48","doi-asserted-by":"crossref","unstructured":"[48] B. Ferwerda, E. Yang, M. Schedl, and M. Tkalcic, \u201cPersonality traits predict music taxonomy preferences,\u201d Proc. 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA 2015, pp.2241-2246, 2015. 10.1145\/2702613.2732754","DOI":"10.1145\/2702613.2732754"},{"key":"49","doi-asserted-by":"crossref","unstructured":"[49] R. Hu and P. Pu, \u201cEnhancing collaborative filtering systems with personality information,\u201d Proc. 5th ACM Conference on Recommender Systems, RecSys 2011, pp.197-204, 2011. 10.1145\/2043932.2043969","DOI":"10.1145\/2043932.2043969"},{"key":"50","doi-asserted-by":"publisher","unstructured":"[50] I. Fern\u00e1ndez-Tob\u0131\u0301as, M. Braunhofer, M. Elahi, F. Ricci, and I. Cantador, \u201cAlleviating the new user problem in collaborative filtering by exploiting personality information,\u201d User Modeling and User-Adapted Interaction, vol.26, no.2, pp.221-255, 2016. 10.1007\/s11257-016-9172-z","DOI":"10.1007\/s11257-016-9172-z"},{"key":"51","unstructured":"[51] F. Lu and N. Tintarev, \u201cA diversity adjusting strategy with personality for music recommendation.,\u201d Proc. 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2018, pp.7-14, 2018."},{"key":"52","doi-asserted-by":"crossref","unstructured":"[52] A.B. Melchiorre, E. Zangerle, and M. Schedl, \u201cPersonality bias of music recommendation algorithms,\u201d Proc. 14th ACM Conference on Recommender Systems, RecSys 2020, pp.533-538, 2020. 10.1145\/3383313.3412223","DOI":"10.1145\/3383313.3412223"},{"key":"53","doi-asserted-by":"crossref","unstructured":"[53] B. Ferwerda and M. Tkal\u010di\u010d, \u201cExploring online music listening behaviors of musically sophisticated users,\u201d Proc. 27th Conference on User Modeling, Adaptation and Personalization, UMAP 2019, pp.33-37, 2019. 10.1145\/3314183.3324974","DOI":"10.1145\/3314183.3324974"},{"key":"54","doi-asserted-by":"crossref","unstructured":"[54] B. Ferwerda, M.P. Graus, A. Vall, M. Tkalcic, and M. Schedl, \u201cHow item discovery enabled by diversity leads to increased recommendation list attractiveness,\u201d Proc. 32nd ACM SIGAPP Symposium on Applied Computing, SAC 2017, pp.1693-1696, 2017. 10.1145\/3019612.3019899","DOI":"10.1145\/3019612.3019899"},{"key":"55","unstructured":"[55] A. LeBlanc, \u201cOutline of a proposed model of sources of variation in musical taste,\u201d Bulletin of the Council for Research in Music Education, no.61, pp.29-34, 1980."},{"key":"56","unstructured":"[56] J.H. Lee and J.S. Downie, \u201cSurvey of music information needs, uses, and seeking behaviours: Preliminary findings,\u201d Proc. 5th International Conference on Music Information Retrieval, ISMIR 2004, pp.441-446, 2004. 10.5281\/zenodo.1417637"},{"key":"57","unstructured":"[57] A. Laplante, \u201cUsers\u2019 relevance criteria in music retrieval in everyday life: An exploratory study,\u201d Proc. 11th International Society for Music Information Retrieval Conference, ISMIR 2010, pp.601-606, 2010. 10.5281\/zenodo.1415578"},{"key":"58","unstructured":"[58] J.H. Lee and N.M. Waterman, \u201cUnderstanding user requirements for music information services,\u201d Proc. 13th International Society for Music Information Retrieval Conference, ISMIR 2012, pp.253-258, 2012. 10.5281\/zenodo.1417625"},{"key":"59","unstructured":"[59] M. Kamalzadeh, D. Baur, and T. M\u00f6ller, \u201cA survey on music listening and management behaviours,\u201d Proc. 13th International Society for Music Information Retrieval Conference, ISMIR 2012, pp.373-378, 2012. 10.5281\/zenodo.1415742"},{"key":"60","unstructured":"[60] K. Tsukuda, M. Hamasaki, and M. Goto, \u201cToward an understanding of lyrics-viewing behavior while listening to music on a smartphone,\u201d Proc. 22nd International Society for Music Information Retrieval Conference, ISMIR 2021, pp.705-713, 2021. 10.5281\/zenodo.5624633"},{"key":"61","doi-asserted-by":"crossref","unstructured":"[61] T. Namikawa, I. Tani, T. Wakita, R. Kumagai, A. Nakane, and H. Noguchi, \u201cDevelopment of a short form of the Japanese Big-Five Scale, and a test of its reliability and validity,\u201d The Japanese Journal of Psychology, vol.83, no.2, pp.91-99, 2012. 10.4992\/jjpsy.83.91","DOI":"10.4992\/jjpsy.83.91"},{"key":"62","doi-asserted-by":"publisher","unstructured":"[62] D. M\u00fcllensiefen, B. Gingras, J. Musil, and L. Stewart, \u201cThe musicality of non-musicians: An index for assessing musical sophistication in the general population,\u201d PLOS ONE, vol.9, no.2, pp.1-23, 2014. 10.1371\/journal.pone.0089642","DOI":"10.1371\/journal.pone.0089642"},{"key":"63","unstructured":"[63] K. Tsukuda, K. Ishida, and M. Goto, \u201cLyric Jumper: A lyrics-based music exploratory web service by modeling lyrics generative process,\u201d Proc. 18th International Society for Music Information Retrieval Conference, ISMIR 2017, pp.544-551, 2017. 10.5281\/zenodo.1417749"},{"key":"64","unstructured":"[64] R. Delbouys, R. Hennequin, F. Piccoli, J. Royo-Letelier, and M. Moussallam, \u201cMusic mood detection based on audio and lyrics with deep neural net,\u201d Proc. 19th International Society for Music Information Retrieval Conference, ISMIR 2018, pp.370-375, 2018. 10.5281\/zenodo.1492427"},{"key":"65","unstructured":"[65] F. Karsdorp, P. van Kranenburg, and E. Manjavacas, \u201cLearning similarity metrics for melody retrieval,\u201d Proc. 20th International Society for Music Information Retrieval Conference, ISMIR 2019, pp.478-485, 2019. 10.5281\/zenodo.3527848"},{"key":"66","unstructured":"[66] M. Hamasaki, K. Ishida, T. Nakano, and M. Goto, \u201cSongrium RelayPlay: A web-based listening interface for continuously playing user-generated music videos of the same song with different singers,\u201d Proc. International Computer Music Conference 2020, ICMC 2020, pp.426-429, 2020."},{"key":"67","unstructured":"[67] A.A. Correya, D. Bogdanov, L. Joglar-Ongay, and X. Serra, \u201cEssentia.js: A JavaScript library for music and audio analysis on the web,\u201d Proc. 21st International Society for Music Information Retrieval Conference, ISMIR 2020, pp.605-612, 2020. 10.5281\/zenodo.4245510"},{"key":"68","unstructured":"[68] G. Micchi, K. Kosta, G. Medeot, and P. Chanquion, \u201cA deep learning method for enforcing coherence in automatic chord recognition,\u201d Proc. 22nd International Society for Music Information Retrieval Conference, ISMIR 2021, pp.443-451, 2021. 10.5281\/zenodo.5624539"},{"key":"69","unstructured":"[69] H.F. Garcia, A. Aguilar, E. Manilow, and B. Pardo, \u201cLeveraging hierarchical structures for few-shot musical instrument recognition,\u201d Proc. 22nd International Society for Music Information Retrieval Conference, ISMIR 2021, pp.220-228, 2021. 10.5281\/zenodo.5624615"},{"key":"70","doi-asserted-by":"publisher","unstructured":"[70] M. Kaminskas and F. Ricci, \u201cContextual music information retrieval and recommendation: State of the art and challenges,\u201d Computer Science Review, vol.6, no.2-3, pp.89-119, 2012. 10.1016\/j.cosrev.2012.04.002","DOI":"10.1016\/j.cosrev.2012.04.002"},{"key":"71","unstructured":"[71] G. Vigliensoni and I. 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