{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:18:10Z","timestamp":1775146690487,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["H2020-761634 FuturePulse"],"award-info":[{"award-number":["H2020-761634 FuturePulse"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Estimating and analyzing the popularity of an entity is an important task for professionals in several areas, e.g., music, social media, and cinema. Furthermore, the ample availability of online data should enhance our insights into the collective consumer behavior. However, effectively modeling popularity and integrating diverse data sources are very challenging problems with no consensus on the optimal approach to tackle them. To this end, we propose a non-linear method for popularity metric aggregation based on geometrical shapes derived from the individual metrics\u2019 values, termed Geometric Aggregation of Popularity metrics (GAP). In this work, we particularly focus on the estimation of artist popularity by aggregating web-based artist popularity metrics. Finally, even though the most natural choice for metric aggregation would be a linear model, our approach leads to stronger rank correlation and non-linear correlation scores compared to linear aggregation schemes. More precisely, our approach outperforms the simple average method in five out of seven evaluation measures.<\/jats:p>","DOI":"10.3390\/info11060323","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T03:17:32Z","timestamp":1592191052000},"page":"323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["GAP: Geometric Aggregation of Popularity Metrics"],"prefix":"10.3390","volume":"11","author":[{"given":"Christos","family":"Koutlis","sequence":"first","affiliation":[{"name":"Information Technologies Institute, Centre of Research and Technology Hellas, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manos","family":"Schinas","sequence":"additional","affiliation":[{"name":"Information Technologies Institute, Centre of Research and Technology Hellas, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5441-7341","authenticated-orcid":false,"given":"Symeon","family":"Papadopoulos","sequence":"additional","affiliation":[{"name":"Information Technologies Institute, Centre of Research and Technology Hellas, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6447-9020","authenticated-orcid":false,"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[{"name":"Information Technologies Institute, Centre of Research and Technology Hellas, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TMM.2018.2820903","article-title":"Music Popularity: Metrics, Characteristics, and Audio-Based Prediction","volume":"20","author":"Lee","year":"2018","journal-title":"IEEE Trans. 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Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Z., Dong, M., Gu, B., Zhang, C., Ji, Y., and Tanaka, Y. (2016, January 5\u20137). Impact of item popularity and chunk popularity in CCN caching management. Proceedings of the 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), Kanazawa, Japan.","DOI":"10.1109\/APNOMS.2016.7737213"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113337","DOI":"10.1016\/j.eswa.2020.113337","article-title":"Discovering patterns of online popularity from time series","volume":"151","author":"Ozer","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dwork, C., Kumar, R., Naor, M., and Sivakumar, D. (2001, January 1\u20135). Ranking aggregation methods for the web. Proceedings of the 10th international conference on World Wide Web, Hong Kong, China.","DOI":"10.1145\/371920.372165"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.procs.2015.10.004","article-title":"Combining Multiple Measures into a Single Figure of Merit","volume":"69","author":"Chignell","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1287\/mnsc.31.6.647","article-title":"A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making)","volume":"31","author":"Brans","year":"1985","journal-title":"Manag. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1057\/jors.1987.44","article-title":"A Reconciliation among Discrete Compromise Solutions","volume":"38","author":"Yoon","year":"1987","journal-title":"J. Oper. Res. Soc."},{"key":"ref_11","unstructured":"Grace, J., Gruhl, D., Haas, K., Nagarajan, M., Robson, C., and Sahoo, N. (2008, January 21\u201325). Artist Ranking Through Analysis of On-line Community Comments. Proceedings of the 17th International World Wide Web Conference, Beijing, China."},{"key":"ref_12","unstructured":"Schedl, M., Pohle, T., Koenigstein, N., and Knees, P. (2010, January 9\u201313). What\u2019s hot? estimating country-specific artist popularity. Proceedings of the International Society for Music Information Retrieval, Utrecht, The Netherlands."},{"key":"ref_13","unstructured":"Schedl, M. (2011, January 16\u201322). Analyzing the Potential of Microblogs for Spatio-Temporal Popularity Estimation of Music Artists. Proceedings of the IJCAI, Barcelona, Spain."},{"key":"ref_14","unstructured":"Mesnage, C., Santos-Rodriguez, R., McVicar, M., and De Bie, T. (2015, January 7\u20139). Trend extraction on Twitter time series for music discovery. Proceedings of the Workshop on Machine Learning for Music Discovery, 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Abel, F., Diaz-Aviles, E., Henze, N., Krause, D., and Siehndel, P. (2010, January 9\u201311). Analyzing the Blogosphere for Predicting the Success of Music and Movie Products. Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining, Odense, Denmark.","DOI":"10.1109\/ASONAM.2010.50"},{"key":"ref_16","unstructured":"Koenigstein, N., and Shavitt, Y. (2009, January 26\u201330). Song Ranking based on Piracy in Peer-to-Peer Networks. Proceedings of the International Society for Music Information Retrieval, Kobe, Japan."},{"key":"ref_17","unstructured":"Bellog\u00edn, A., de Vries, A.P., and He, J. Artist Popularity: Do Web and Social Music Services Agree? In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Cambridge, MA, USA, 8\u201311 July 2013."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ren, J., Shen, J., and Kauffman, R.J. (2016, January 11\u201315). What Makes a Music Track Popular in Online Social Networks?. Proceedings of the 25th International Conference Companion on World Wide Web, Montreal, QC, Canada.","DOI":"10.1145\/2872518.2889402"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kim, Y., Suh, B., and Lee, K. (2014, January 11). #Nowplaying the Future Billboard: Mining Music Listening Behaviors of Twitter Users for Hit Song Prediction. Proceedings of the First International Workshop on Social Media Retrieval and Analysis, Gold Coast, Australia.","DOI":"10.1145\/2632188.2632206"},{"key":"ref_20","unstructured":"Koutlis, C., Schinas, M., Gkatziaki, V., Papadopoulos, S., and Kompatsiaris, Y. (2019, January 4\u20138). Data-driven song recognition estimation using collective memory dynamics models. Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR), Delft, The Netherlands."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1037\/1082-989X.12.4.399","article-title":"Toward Using Confidence Intervals to Compare Correlations","volume":"12","author":"Zoo","year":"2007","journal-title":"Psychol. Methods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1111\/j.0006-341X.2000.01134.x","article-title":"A Permutation Test to Compare Receiver Operating Characteristic Curves","volume":"56","author":"Venkatraman","year":"2000","journal-title":"Biometrics"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Diedenhofen, B., and Musch, J. (2015). cocor: A Comprehensive Solution for the Statistical Comparison of Correlations. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0121945"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. 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