%0 Journal Article %1 bonnin2014automated %A Bonnin, Geoffray %A Jannach, Dietmar %C New York, NY, USA %D 2014 %I ACM %J ACM Computer Surveys (CSUR) %K diss generation inthesis music playlist prediction recommendation review song survey %N 2 %P 26:1--26:35 %R 10.1145/2652481 %T Automated Generation of Music Playlists: Survey and Experiments %U http://doi.acm.org/10.1145/2652481 %V 47 %X Most of the time when we listen to music on the radio or on our portable devices, the order in which the tracks are played is governed by so-called playlists. These playlists are basically sequences of tracks that traditionally are designed manually and whose organization is based on some underlying logic or theme. With the digitalization of music and the availability of various types of additional track-related information on the Web, new opportunities have emerged on how to automate the playlist creation process. Correspondingly, a number of proposals for automated playlist generation have been made in the literature during the past decade. These approaches vary both with respect to which kind of data they rely on and which types of algorithms they use. In this article, we review the literature on automated playlist generation and categorize the existing approaches. Furthermore, we discuss the evaluation designs that are used today in research to assess the quality of the generated playlists. Finally, we report the results of a comparative evaluation of typical playlist generation schemes based on historical data. Our results show that track and artist popularity can play a dominant role and that additional measures are required to better characterize and compare the quality of automatically generated playlists.

@article{bonnin2014automated, abstract = {Most of the time when we listen to music on the radio or on our portable devices, the order in which the tracks are played is governed by so-called playlists. These playlists are basically sequences of tracks that traditionally are designed manually and whose organization is based on some underlying logic or theme. With the digitalization of music and the availability of various types of additional track-related information on the Web, new opportunities have emerged on how to automate the playlist creation process. Correspondingly, a number of proposals for automated playlist generation have been made in the literature during the past decade. These approaches vary both with respect to which kind of data they rely on and which types of algorithms they use. In this article, we review the literature on automated playlist generation and categorize the existing approaches. Furthermore, we discuss the evaluation designs that are used today in research to assess the quality of the generated playlists. Finally, we report the results of a comparative evaluation of typical playlist generation schemes based on historical data. Our results show that track and artist popularity can play a dominant role and that additional measures are required to better characterize and compare the quality of automatically generated playlists.}, acmid = {2652481}, added-at = {2017-01-16T14:29:07.000+0100}, address = {New York, NY, USA}, articleno = {26}, author = {Bonnin, Geoffray and Jannach, Dietmar}, biburl = {https://www.bibsonomy.org/bibtex/27764b576bd1745704fcb51e34101e532/becker}, doi = {10.1145/2652481}, interhash = {4f14a668ac069455041b37ed18ae5df6}, intrahash = {7764b576bd1745704fcb51e34101e532}, issn = {0360-0300}, issue_date = {January 2015}, journal = {ACM Computer Surveys (CSUR)}, keywords = {diss generation inthesis music playlist prediction recommendation review song survey}, month = nov, number = 2, numpages = {35}, pages = {26:1--26:35}, publisher = {ACM}, timestamp = {2017-12-20T15:14:41.000+0100}, title = {Automated Generation of Music Playlists: Survey and Experiments}, url = {http://doi.acm.org/10.1145/2652481}, volume = 47, year = 2014 }