Overview

1. MegsRadioPromoting local music and events through adaptive recommendation algorithmsAndrew HorwitzMUMT 6213/26/2014

2. Overview • Look at previous research into playlist compilation/music recommendation • The MegsRadioproject; our algorithms • Our goals

3. Playlist RecommendationFields (2011) • Playlist structure is (unconsciously) influenced by Tversky (1977) – songs are considered as objects with properties that are considered similar to other objects. • MIREX has had an Audio Music Similarity and Retrieval (AMS) task to compare playlist algorithms. • “Given a collection of 7,000 30-second clips of audio… for each selected query track, find the 100 most similar tracks in the correct order and assigned the correct similarity score.”

4. Playlist RecommendationFields (2011) • Music Recommendation vs. Playlist Generation • Raw recommendation is similarity between users: usually relies on drawing correlation between ratings (Amazon) • Playlist generation focuses more on similarity between songs • Focuses on/in playlist generation: • Art of the Mix • Patterns over explicit similarity • Cultural and situational aspects

5. Automatic Playlist ConstructionMcFee and Lanckriet (2011) • Uniform shuffle • Songs selected randomly, without weight, from a given subset. • Refined by disallowing consecutive repetitions: for a song xt, xt+1 is drawn fromX \ {xt} • Weighted shuffle • Songs weighted by various characteristics • Can be “steered” by tags/user feedback (Maillet 2009) • k-NN and random walks • Songs organized with neighbor relationships by some characteristics, random walk performed • Markov chains • System intelligently determines weights for weighted shuffle/parameters for k-NN maps

6. Steerable PlaylistsMaillet et al. (2009) • Built a 180-dimensional similarity web • Based off radio programs, song-level characteristics and smaller-frame characteristics • Seed track is selected by user; tracks most similar to that song (above a threshold or to a certain quantity) are put aside • User refines these similarities by putting weights on any of 360 tags

7. Steerable PlaylistsMaillet et al. (2009) • Different tags lead to different results;both playlists are seeded with the song Clumsy by Our Lady Peace • “Soft” tag cloud is made up of the tags for Imagine by John Lennon • “Hard” tag cloud with the tags for Hypnotize by System of a Down

8. What’s missing from internet radio • Increase awareness of local music • Contextualize with mainstream music • Focus on event promotion • Either tickets OR music, rarely linked • More tunable parameters • Feedback sometimes limited to only +/- • Craigslist model

9. Our playlist algorithm • Seeded by a combination of tags and artists • Gets a list of: • all songs that are by artists similar to the seed artists, or have positive correlations to the tags • all songs listened to by the user and all related feedback data • (dislike/WTF/discovery/like/ban) • user’s station preferences (Echonest characteristics and a few others) • Correlates the three lists and adds/detracts value or removes songs accordingly

10. Our playlist algorithm • Master playlist vs. (more|some|less) • Popular music: Echonest popularity statistics • Repeats: based off listening history for the user on current station and based on DMCA guidelines • Local music: different weights when selecting songs…

11. Our playlist algorithm • Makes a list of all songs by seeded artists and a list of all songs by local artists • Picks the highest-weighted song by a seeded artist, highest-weighted local song, and highest-weighted song that is not those two • Randomly selects songs until a certain length is reached – there are parameters for how likely a local song is or a seed artist song is. • Statistical noise is introduced at various parts in the process to ensure variety if the playlist needs to be reloaded for some reason

12. Event recommendation • “Approximately how many music events do you attend per month?” • We want to increase this – as users are exposed to more local bands, they should find more events they want to attend. • List of events pulled in via webcrawlers tailored to local news sites/event promoters • Same list of all tracks listened to and of all feedback provided through all stations

13. Event recommendation • If the user has listened to or provided feedback on an artist that is performing at an event, their opinion is weighted by a flat amount • If the user has listened to or provided feedback on an artist similar to a performer, the opinion is weighted by how similar the artist is to the performers. • Added rather than averaged: we figured that if a user liked multiple artists at an event, they’d like the event even more • Highest-scoring artist (after similarity multipliers are calculated) is given as a “Why we think you’d like this event” reason

15. Promoting local music and events • Both algorithms are linked to the same data • listens affect event recommendations • different events suggested using same similarity data in different cities • Visual interplay • radio interface has links to events: “This artist is playing in $CITY_NAME soon!” • event interface has “Start playlist” for each event • Local music interspersed with mainstream • recommendations can be based off both • events for both are shown

16. Future research • Plug-and-Playlist™ • No Markov “smart” implementation yet • Testing different parameters on our recent revisions • API construction means we can dynamically switch between algorithms • “Approximately how many music events do you attend per month?” • Do we help increase this number? How can we?

17. References • Fields, B. 2011. Contextualize your listening: the playlist as recommendation engine. Ph.D. Dissertation. University of London. • Maillet, F., D. Eck, G. Desjardins, and P. Lamere. 2009. Steerable playlist generation by learning song similarity from radio station playlists. Proceedings of the International Society for Music Information Retrieval Conference. Kobe, Japan. 345-50. • McFee, B., L. Barrington, and G. Lanckriet. 2012. Learning content similarity for music recommendation. IEEE Transactions onAudio, Speech, and Language Processing 20(8). 2207-18. • McFee, B., and G. Lanckriet. 2011. The natural language of playlists. Proceedings of the International Society for Music Information Retrieval Conference. Miami, FL. 537-42. • Turnbull, D., L. Barrington, D. Torres, and G. Lanckriet. (2008) Semantic annotation and retrieval of music and sound effects. IEEE Transactions onAudio, Speech, and Language Processing 16(2). 467-76. • Tversky, A. 1977. Features of similarity. Psychological review 84(4). 327-52.