An Analysis of Global and Regional Mainstreaminess for Personalized Music Recommender Systems
DOI:
https://doi.org/10.13052/1550-4646.1415Keywords:
music mainstreaminess, music recommender systems, artist frequency-inverse listener frequency, popularity, country-specific differencesAbstract
The music mainstreaminess of a listener reflects how strong a person’s listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives, we show country-specific differences and study how taking into account music mainstreaminess influences the quality of music recommendations. In this paper, we first propose 11 novel mainstreaminess measures characterizing music listeners, considering both a global and a countryspecific basis for mainstreaminess. To this end, we model preference profiles (as a vector over artists) for users, countries, and globally, incorporating artist frequency, listener frequency, and a newly proposed TF-IDF-inspired weighting function, which we call artist frequency–inverse listener frequency (AF-ILF). The resulting preference profile for each user u is then related to the respective country-specific and global preference profile using fraction-based approaches, symmetrized Kullback-Leibler divergence, and Kendall’s τ rank correlation, in order to quantify u’s mainstreaminess. Second, we detail country-specific peculiarities concerning what defines the countries’ mainstream and discuss the proposed mainstreaminess definitions. Third, we show that incorporating the proposed global and country-specific mainstreaminess measures into the music recommendation process can notably improve accuracy of rating prediction.
Downloads
References
Ahn, H. J. (2006). Utilizing popularity characteristics for product recommendation.
International Journal of Electronic Commerce, 11(2),
–80.
Anderson, C. (2006). The long tail: Why the future of business is selling
more for less. Hyperion.
Baek, Y. M. (2015). Relationship between cultural distance and
cross-cultural music video consumption on YouTube. Social Science
Computer Review, 33(6), 730–748.
Baker, S., Bennett, A., and Taylor, J. (Eds.). (2013). Redefining
mainstream popular music. Routledge.
Basu, C., Hirsh, H., and Cohen,W. (1998). Recommendation as classification:
Using social and content-based information in recommendation.
In Proceedings of the AAAI Conference on Artificial Intelligence,
–720. American Association Intelligence, 1998.
Bauer, C., Kholodylo, M., and Strauss C. (2017). Music recommender
systems: Challenges and opportunities for non-superstar artists.
In Andreja Pucihar, Mirjana Kljaji´c Bor˘stnar, Christian Kittl, Pascal
Ravesteijn, Roger Clarke, and Roger Bons, editors, Proceedings of 30th
Bled eConference, 21–32.
Bauer, C., and Schedl, M. (2018). On the importance of considering
country-specific aspects on the online-market:An example of music recommendation
considering country-specific mainstream. In 51st Hawaii
International Conference on System Sciences (HICSS 3647–3656.
Bawden, D., and Robinson, L. (2009). The dark side of information:
overload, anxiety and other paradoxes and pathologies. Journal of
Information Science, 35(2), 180–191.
Brynjolfsson, E., Hu, Y., and Simester, D. (2011). Goodbye pareto
principle, hello long tail: The effect of search costs on the concentration
of product sales. Management Science, 57(8), 1373–1386.
Budzinski, O., and Pannicke, J. (2017). Do preferences for pop music
converge across countries–Empirical evidence from the Eurovision
Song Contest. Creative Industries Journal, 1–20, 2017.
Celma, O. (2010). Music recommendation. In Music recommendation
and discovery, 43–85. Springer, Berlin, Heidelberg.
Celma, Ò., and Cano, P. (2008). From hits to niches: or how popular
artists can bias music recommendation and discovery. In Proceedings
of the 2nd KDD Workshop on Large-Scale Recommender Systems and
the Netflix Prize Competition, 5.
Cheng, Z., and Shen, J. (2014). Just-for-me: An adaptive personalization
system for location-aware social music recommendation. In
Proceedings of international conference on multimedia retrieval, 185.
Clarke, C. L., Kolla, M., Cormack, G. V., Vechtomova, O., and Ashkan,
A. Stefan B üttcher, and Ian MacKinnon. (2008). Novelty and diversity
in information retrieval evaluation. In Proceedings of SIGIR, 659–666).
Cremonesi, P., Garzotto, F., Pagano, R., and Quadrana, M. (2014). Recommending
without short head. In Proceedings of the 23rd International
Conference on World Wide Web. 245–246).
Farrahi, K., Schedl, M., Vall, A., Hauger, D., and Tkalˇciˇc, M. (2014).
Impact of listening behavior on music recommendation. In Proceedings
of the 15th International Society for Music Information Retrieval
Conference, 483–488.
Ferwerda, B. (2016). Improving the User Experience of Music Recommender
Systems Through Personality and Cultural Information. PhD.
Johannes Kepler University Linz, Linz, Austria.
Hauger, D., Schedl, M., Košir, A., and Tkalcic, M. (2013). The million
musical tweets dataset: what can we learn from microblogs. In Proc.
ISMIR, 189–194.
Hracs, B. J., Seman, M., and Virani, T. E. (2016). The production and
consumption of music in the digital age, Abingdon: Routledge, 58.
Hu, X., Lee, J. H., Choi, K., and Downie, J. S. (2014). A cross-cultural
study of mood in k-pop songs. In Proceedings of the 15th International
Society for Music Information Retrieval Conference, ISMIR 217–238,
Jones, M. L. (2007). Hofstede-culturally questionable. In Proceedings
of the Oxford Business & Economics Conference (OBEC).
Kitayama, S., and Park, H., (2007). Cultural shaping of self, emotion,
and well-being: How does it work? Social and Personality Psychology
Compass, 1(1) 202–222.
Kumar, R., Verma, B. K., and Rastogi, S. S. (2014). Social popularity
based SVD++ recommender system. International Journal of Computer
Applications, 87(14).
Laplante, A. (2014). Improving music recommender systems: what
can we learn from research on music tags? In Proceedings of the
th International Society for Music Information Retrieval Conference,
ISMIR 451–456.
Lee, J. H., and Hu, X. (2014). Cross-cultural similarities and differences
in music mood perception. iConference 2014 Proceedings. Linden,
Linden, G., Smith, B., and York, J. (2003). Amazon. com recommendations:
Item-to-item collaborative filtering. IEEE Internet computing,
(1), 76-80.
McFee, B., Barrington, L., and Lanckriet, G. (2012). Learning content
similarity for music recommendation. IEEE transactions on audio,
speech, and language processing, 20(8), 2207–2218.
McSweeney, B. (2002). Hofstede’s model of national cultural differences
and their consequences: A triumph of faith-a failure of analysis.
Human relations, 55(1), 89–118.
Montaner, M., López, B., and De La Rosa, J. L. (2003). A taxonomy
of recommender agents on the internet. Artificial intelligence review,
(4), 285–330.
Morrison, S. J., and Demorest, S. M. (2009). Cultural constraints on
music perception and cognition. Progress in brain research, 178, 67–77.
Pichl, M., Zangerle, E., and Specht, G. (2015). Towards a contextaware
music recommendation approach: What is hidden in the playlist
name?. In Data Mining Workshop (ICDMW), 2015 IEEE International
Conference on 1360–1365.
Pohle, T., Knees, P., Schedl, M., andWidmer, G. (2006). Automatically
adapting the structure of audio similarity spaces. In Proc. 1st Workshop
on Learning the Semantics of Audio Signals (LSAS), 66–75.
Power And, D., and Hallencreutz, D. (2007). Competitiveness, local
production systems and global commodity chains in the music industry:
entering the US market. Regional Studies, 41(3), 377-389.
Ricci, F. (2015). Recommender Systems Handbook: /Francesco Ricci,
Lior Rokach, Bracha Shapira–Springer Science+ Business Media New
York, 1003 p. ISBN 978-1-4899-7636-9.
Rutten, P. (1991). Local popular music on the national and international
markets. Cultural Studies, 5(3) 294–305.
Salakhutdinov, R., and Mnih, A. (2007). Probabilistic Matrix Factorization.
In Proceedings of the 20th International Conference on Neural
Information Processing Systems, 1257–1264.
Salton, G., Wong, A., and Yang, C. S. (1975). A vector space model for
automatic indexing. Communications of the ACM, 18(11), 613–620.
Schedl, M. (2013). Ameliorating music recommendation: Integrating
music content, music context, and user context for improved
music retrieval and recommendation. In Proceedings of International
Conference on Advances in Mobile Computing & Multimedia,
Schedl,M.(2016).The lfm-1b dataset for music retrieval and recommendation.
In Proceedings of the 2016 ACM on International Conference
on Multimedia Retrieval, 103–110.
Schedl, M. (2017). Investigating country-specific music preferences
and music recommendation algorithms with the LFM-1b dataset.
International journal of multimedia information retrieval, 6(1), 71–84.
Schedl, M., and Bauer, C. (2017). Distance-and Rank-based Music
Mainstreaminess Measurement. In Adjunct Publication of the 25th
Conference on User Modeling, Adaptation and Personalization,
–367.
Schedl, M., and Ferwerda, B. (2017). Large-scale Analysis of Groupspecific
Music Genre Taste From Collaborative Tags. In The 19th IEEE
International Symposium on Multimedia (ISM2017), Taichung.
Schedl, M., Gómez, E., and Urbano, J. (2014). Music information
retrieval: Recent developments and applications. Foundations and
Trends in Information Retrieval, 8(2–3), 127–261.
Schedl, M., and Hauger, D. (2015). Tailoring music recommendations
to users by considering diversity, mainstreaminess, and novelty. In Proceedings
of the 38th International ACM SIGIR Conference on Research
and Development in Information Retrieval, 947–950.
Schedl, M., Knees, P., McFee, B., Bogdanov, D., and Kaminskas,
M. (2015). Music recommender systems. In Recommender Systems
Handbook, 453–492.
Singhi, A., and Brown, D. G. (2014). On Cultural, Textual and
Experiential Aspects of Music Mood. In ISMIR, 3–8.
Stevens, C. J. (2012). Music perception and cognition:Areview of recent
cross−cultural research. Topics in cognitive science, 4(4), 653–667.
Vigliensoni, G., and Fujinaga, I. (2016). Automatic Music Recommendation
Systems: Do Demographic, Profiling, and Contextual Features
Improve Their Performance. In ISMIR, 94–100.
Xiao, L., Lu, L., Seide, F., and Zhou, J. (2009). Learning a music
similarity measure on automatic annotations with application to playlist
generation. In Acoustics, Speech and Signal Processing, 2009. ICASSP
IEEE International Conference on, 1885–1888.
Yan, Y., Liu, T., andWang, Z. (2015). A Music Recommendation Algorithm
Based on Hybrid Collaborative Filtering Technique. In Chinese
National Conference on Social Media Processing, 233–240.
Yang, J. (2016). Effects of popularity-based news recommendations
(“most-viewed”) on users’ exposure to online news. Media Psychology,
(2), 243–271.
Zhang, Y. C., Séaghdha, D. Ó., Quercia, D., and Jambor, T. (2012).
Auralist: introducing serendipity into music recommendation. In Proceedings
of the fifth ACM international conference on Web search and
data mining, 13–22.
