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From Improved Auto-Taggers to Improved Music Similarity Measures., , , , and . Adaptive Multimedia Retrieval, volume 8382 of Lecture Notes in Computer Science, page 193-202. Springer, (2012)Mining microblogs to infer music artist similarity and cultural listening patterns., and . WWW (Companion Volume), page 877-886. ACM, (2012)State of the Art of Adaptivity in E-Learning Platforms., and . page 355--360. (2007)Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework., , and . Multim. Syst., 20 (6): 693-705 (2014)Tailoring Music Recommendations to Users by Considering Diversity, Mainstreaminess, and Novelty., and . SIGIR, page 947-950. ACM, (2015)Using Asynchronous Client-Side User Monitoring to Enhance User Modeling in Adaptive E-Learning Systems.. AP WEB 2.0@UMAP, volume 485 of CEUR Workshop Proceedings, CEUR-WS.org, (2009)Impact of Listening Behavior on Music Recommendation., , , , and . ISMIR, page 483-488. (2014)On the Influence of User Characteristics on Music Recommendation Algorithms., , , and . ECIR, volume 9022 of Lecture Notes in Computer Science, page 339-345. (2015)Fine-grained user models by means of asynchronous web technologies.. LWA, volume 448 of Technical Report, page 17-19. Department of Computer Science, University of Würzburg, Germany, (2008)The Million Musical Tweet Dataset - What We Can Learn From Microblogs., , , and . ISMIR, page 189-194. (2013)