Smart Party: Song Selection

1. Smart Party: Song Selection Kevin F. Eustice, Jason Stoops, Peter Reiher, Venkatraman Ramakrishna, Nam Nguyen UCLA

2. Voting Algorithm Overview (Eustice)‏ • Each device nominates a song to be played next • A ballot containing metadata of all these songs is sent out to all devices • From a pool of points, devices then assign points to each song in ballot proportional to personal preference value associated with song • Points are tallied by coordinator and the bottom half of candidates are dropped • Process repeats until one song wins!

3. Point Assignment • Matches attempted by devices for point assignment when filling out ballot: • Filename match --> points based on song preference • Artist, title match --> points based on song preference • Artist match --> based on average preference for artist • Genre match --> based on average preference for that genre • No match --> no points allocated to this candidate • Averages for known genres and artists are computed at application startup

4. Ballot Compilation • Logical ballot compilation ensures correct point distribution during vote: • Songs nominated by two different devices --> one ballot entry • Songs with same metadata but different filename --> one ballot entry • Songs with empty metadata --> separate ballot entries. • Devices can look at history of songs played in room and songs heard by device, and nominate unheard songs. (LASR)‏

5. Robustness • Vote coordination is done by application separate from Smart Party Location Application, or SPLA. • SPLA has had reliable Round Robin song selection algorithm built in since beginning (LASR). • If something goes wrong in voting, SPLA can detect this condition and fails over to Round Robin selection. • Smart Party User Device Application, or SPUDA, is selection algorithm agnostic. • simply responds to various requests

6. Accomplishments for quarter • Implement voting algorithm • Learn basics of Panoply framework • Build up side-by-side implementation of voting • Initially entirely separate from Smart Party application • Integrate voting into SPUDA • Vote Leader is still separate for future work in Smart Party: • Vote coordination may eventually be handled by SPUDAs • Vote leader may be chosen dynamically in future

7. Accomplishments for quarter, cont. • Matching for metadata and tracking average preference value for known artists, genres • Fail over to Round Robin selection if vote coordinator dies for some reason • Generally complete implementation of individual voting song selection for a multi-room Smart Party.

8. An interesting test • Tested voting algorithm with 6 participants • Varied media libraries and preferences • Four people liked various songs by Artist A • Not all people had all songs by artist, some had exclusive sets of songs. • Two other people had a CD by another Artist B • Overall, most music fell under Genre A. • Result • Songs by Artist A in Genre A played first, then other songs in Genre A.

9. Satisfaction Of Proposed Goals • Satisfied Checkpoint 1: implement voting algorithm in smart party • Algorithm is in use in 3 room demo in LASR lab • Laptops can connect, and party will select songs from users in each room. • However, checkpoint 2 has changed significantly...

10. Next quarter... Checkpoint 2 • Formulate user mobility model • How does music affect people moving between rooms in the party? • How does this affect fairness and user satisfaction of song selection algorithms? • Investigate media-centric rooms vs. user-centric rooms • Look at ALL music in party vs. music in room • Create rooms based on preference groups • “Rock” room, “Jazz” room, “Beatles” room, etc. • Devices may suggest a room to user based on prefs

11. Acknowledgements • Venkatraman Ramakrishna, UCLA LASR, for helping and guiding me during this project. • Kevin F. Eustice, Nam Nguyen, and Peter Reiher, UCLA LASR