Abstract
Through user accounts, music recommendations are refined by user-supplied genres and artists preferences. Music recommendation is further complicated by multiple genre artists, artist collaborations and artist similarity identification. We focus primarily on artist similarity in which we propose a rank fusion solution. We aggregate the most similar artist ranking from Idiomag, Last.fm and Echo Nest Web APIs. Each Web API and resulting ranked list using a rank fusion algorithm has a different method of relating their artists. Through an experimental evaluation of nearly 500 artist queries, we first identify a ground truth for artist similarity for comparison to five rank fusion algorithms and conduct a performance analysis to discern how well relevant information can be retrieved from biased multiple sources. By understanding this overlap, we can more easily isolate the multiple genre artists and artist collaborations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This chapter is an expanded version of the original publication “Aggregating Music Recommendation Web APIs by Artist” in the Proceedings of the International Conference on Information Reuse and Integration 2010, pages 75–79.
- 2.
- 3.
- 4.
- 5.
References
Adalı, S., Hill, B., Magdon-Ismail, M.: Information vs. robustness in rank aggregation: Models, algorithms and a statistical framework for evaluation. Special Issue on Web Information Retrieval, J. Digit. Inform. Manage. 5(5), 292–307 (2007)
Bischoff, K., Firan, C., Nejdl, W., Paiu, T.: Can all tags be used for search. In: Proceedings of ACM CIKM, pp. 203–212 (2008)
Borda, J.C.: Mémoire sur les élections au scrutin. In Histoire de l’Académie Royale des Sciences (1781)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of ACM WWW, pp. 107–117 (1998)
Chen, H.-C., and Chen, A.L.P.: A music recommendation system based on music data grouping and user interests. In: Proceedings of ACM CIKM, pp. 231–238 (2001)
Chen, L., Wright, P., Nejdl, W.: Improving music genre classification using collaborative tagging data. In: Proceedings of the ACM International Conference on Web Search and Data Mining, pp. 84–93 (2009)
Cunningham, S., Bainbridge, D., McKay, D.: Finding new music: a diary study of everyday encounters with novel songs. In International Conference on Music Information Retrieval (ISMIR) pp. 83–88 (2007)
Dikerson, K.B., Ventura, D.: Music recommendation and query-by-content using self organizing maps. In: Proceedings of the International Joint Conference on Neural Networks pp. 705–710 (2009)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of ACM WWW pp. 613–622 (2001)
Ellis, D.P.W., Whitman, B., Berenzweig, A., Lawrence, S.: The quest for ground truth in musical artist similarity. In: Proceedings of the International Symposium on Music Information Retrieval pp. 170–177 (2002)
Fagin, R., Kumar, R., Sivakumar, D.: Efficient similarity search and classification via rank aggregation. In: Proceedings of ACM SIGMOD pp. 301–312 (2003)
Geleijnse, G., Schedl, M., Knees, P.: The quest for ground truth in musical artist tagging in the social web era. In: International Conference on Music Information Retrieval(ISMIR) pp. 525–530 (2007)
Im, I., Hars, A.: Does a one-size recommendation system fit all? the effectiveness of collaborative filtering based recommendation systems across different domains and search modes. ACM Trans. Inform. Syst. 26(1), (2007)
Knees, P., Pampalk, E., Widmer, G.: Artist classification with web-based data. In: Proceedings of the International Symposium on Music Information Retrieval pp. 517–524 (2004)
Lee, J.H.: Analyses of multiple evidence combination. In: Proceedings of ACM SIGIR pp. 267–276 (1997)
Magno, T., Sable, C.: A comparison of signal-based music recommendation to genre labels, collaborative filtering, musicological analysis, human recommendation, and random baseline. In: International Conference on Music Information Retrieval(ISMIR) pp. 161–166 (2008)
McKay, C., Fuhinaga, I.: Improving automatic music classification performance by extracting features from different types of data. In: Proceedings of the ACM SIGMM International Conference on Multimedia Information Retrieval pp. 257–266 (2010)
Montague, M., Aslam, J.A.: Condorcet fusion for improved retrieval. In: Proceedings of ACM CIKM pp. 538–548 (2002)
Shao, B., Li, T., Ogihara, M.: Quantity music artist similarity based on style and mood. In: Proceedings of the ACM International Workshop on Web Information and Data Management pp. 119–124 (2008)
Shen, J., Shepard, J., Cui, B., Tan, K.L.: A novel framework for efficient automated singer identification in large music databases. ACM Trans. Inform. Syst. 27(3), (2009)
Slaney, M., White, W.: Measuring playlist diversity for recommendation systems. In: Proceedings of the ACM Workshop on Audio and Music Computing Multimedia pp. 77–82 (2006)
Turnbull, D., Barrington, L., Lanckreit, G.: Five approaches to collecting tags for music. In: International Conference on Music Information Retrieval (ISMIR) pp. 225–230 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Vienna
About this chapter
Cite this chapter
Marshall, B. (2012). Music Artist Similarity Aggregation. In: Özyer, T., Kianmehr, K., Tan, M. (eds) Recent Trends in Information Reuse and Integration. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0738-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-7091-0738-6_15
Published:
Publisher Name: Springer, Vienna
Print ISBN: 978-3-7091-0737-9
Online ISBN: 978-3-7091-0738-6
eBook Packages: Computer ScienceComputer Science (R0)