MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts (CSCW98)

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MusicFX is an example of an active environment that uses a group preference arbitration system to allow the members of a fitness center to influence, but not directly control, the selection of music in that environment. The system contains a database of members' musical preferences, a badge system for determining who is working out, and a weighted random selection algorithm for selecting music to best suit the group inhabitants at any given time. MusicFX was deployed in the fitness center at Accenture Technology Park in Northbrook, IL (USA) from November 1997 through January, 2002. These slides are from the CSCW 98 presentation on the system. More info, including the CSCW 98 paper, can be found at


<ul><li> 1. MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts Joe McCarthy Ted Anagnost Andersen Consulting Center for Strategic Technology Research</li></ul> <p> 2. Outline </p> <ul><li>UbiComp &amp; Intelligent Environments </li></ul> <ul><li>The MusicFX System </li></ul> <ul><li>Evaluating Group Preference Arbitration </li></ul> <ul><li>Future Work </li></ul> <p> 3. Ubiquitous Computing </p> <ul><li>Proliferation of networked devices </li></ul> <ul><li><ul><li>phones, TVs, cam's, mic's, microwaves, refrigerators... </li></ul></li></ul> <ul><li>Distribution of computing resources </li></ul> <ul><li><ul><li>portable, wearable, embedded </li></ul></li></ul> <ul><li>New paradigm of computing </li></ul> <ul><li><ul><li>input/output ( foreground ) --&gt;sense/respond ( background ) </li></ul></li></ul> <p> 4. Intelligent Environments </p> <ul><li> UbiComp in a box </li></ul> <ul><li><ul><li>Interconnected, cooperating devices </li></ul></li></ul> <ul><li><ul><li>Concentrated in a small area (e.g., one room) </li></ul></li></ul> <ul><li>Redefining HCI </li></ul> <ul><li><ul><li>users --&gt;inhabitants </li></ul></li></ul> <p> 5. Observation 1 </p> <ul><li>Most UbiComp applications focus on single individualsinmultiple spaces </li></ul> <ul><li><ul><li>Active Badge: open doors, teleporting </li></ul></li></ul> <ul><li><ul><li>ParcTab: information access, email </li></ul></li></ul> <ul><li>What aboutmultiple inhabitantsin a single, shared space ? </li></ul> <p> 6. Observation 2 7. Music in the Fitness Center (FX) </p> <ul><li>Popular in the Complaint Department </li></ul> <ul><li><ul><li>25% of feedback focused on music </li></ul></li></ul> <ul><li>RSI:Repetitive Song Injury</li></ul> <ul><li><ul><li>3 stations played, 91 available (DMX) </li></ul></li></ul> <ul><li>Squeaky Wheels </li></ul> <ul><li><ul><li>Vocal minority prevails over silent majority </li></ul></li></ul> <ul><li>Hangovers </li></ul> <ul><li><ul><li>This mornings music = last nights music </li></ul></li></ul> <p> 8. Four Issues for any Intelligent Environment </p> <ul><li>Whos here? </li></ul> <ul><li>What are they doing? </li></ul> <ul><li>What are their preferences? </li></ul> <ul><li>What can I do to help? </li></ul> <p> 9. Four Issues for MusicFX </p> <ul><li>Whos here? </li></ul> <ul><li><ul><li>Members who login [badge reader] </li></ul></li></ul> <ul><li>What are they doing? </li></ul> <ul><li><ul><li>Working out while listening to music </li></ul></li></ul> <ul><li>What are their preferences? </li></ul> <ul><li><ul><li>Diverse (to say the least) </li></ul></li></ul> <ul><li>What can I do to help? </li></ul> <ul><li><ul><li>Play good music </li></ul></li></ul> <p> 10. The MusicFX System </p> <ul><li>Database of musical preferences </li></ul> <ul><li>Group Preference Arbitration algorithm </li></ul> <ul><li><ul><li>Group Preference Calculation </li></ul></li></ul> <ul><li><ul><li>Candidate Identification </li></ul></li></ul> <ul><li><ul><li>Weighted Random Selection operator </li></ul></li></ul> <p> 11. Music Preference Database </p> <ul><li>275 fitness center members </li></ul> <ul><li>91 musical genres (DMX stations) </li></ul> <ul><li>5-point rating scale </li></ul> <ul><li><ul><li>+2= Ilovethis music </li></ul></li></ul> <ul><li><ul><li>+1= Ilikethis music </li></ul></li></ul> <ul><li><ul><li>0= Idont mindthis music </li></ul></li></ul> <ul><li><ul><li>-1= Idislikethis music </li></ul></li></ul> <ul><li><ul><li>-2= Ihatethis music </li></ul></li></ul> <p> 12. Group Preference Arbitration </p> <ul><li>Group Preference Calculation </li></ul> <ul><li>Candidate Identification </li></ul> <ul><li>Weighted Random Selection</li></ul> <p> 13. Group Preference Calculation </p> <ul><li>Where </li></ul> <ul><li>GP i=G roupP reference for genrei </li></ul> <ul><li>IP i,j=I ndividualP reference of personjfor genrei </li></ul> <ul><li>N =N umber of inhabitants </li></ul> <p> 14. Candidate Identification </p> <ul><li>Sort genre list byGP i </li></ul> <ul><li>Remove any undesireable genre</li></ul> <ul><li><ul><li>Individual Preference Filter </li></ul></li></ul> <ul><li>Candidates are the first M genre </li></ul> <ul><li><ul><li>Group Preference Filter </li></ul></li></ul> <p> 15. Weighted Random Selection </p> <ul><li>Calculate weights for candidates </li></ul> <ul><li>Probabilistically select genre according toW i </li></ul> <p> 16. An example 17. Environmental Events </p> <ul><li>Member entrance </li></ul> <ul><li><ul><li>Login (badge reader) </li></ul></li></ul> <ul><li>Member exit </li></ul> <ul><li><ul><li>Timeout (90 minutes) </li></ul></li></ul> <ul><li>Individual Preference Update </li></ul> <ul><li>System Parameter Adjustment </li></ul> <ul><li><ul><li>Individual / Group Preference Filter, Maximum Play Time </li></ul></li></ul> <ul><li>Maximum Play Time Elapsed </li></ul> <p> 18. The Success of MusicFX</p> <ul><li>Daily operation since November 1997 </li></ul> <ul><li>Poll results (after 6 weeks) </li></ul> <ul><li><ul><li> : increased variety, having some influence </li></ul></li></ul> <ul><li><ul><li> : abrupt changes, occasional bad music </li></ul></li></ul> <p> 19. EvaluatingGroup Preference Arbitration </p> <ul><li>Calculate the goodness of MusicFX </li></ul> <ul><li>Estimate the goodness of old scheme </li></ul> <ul><li>Compare the old with the new </li></ul> <p> 20. The Goodness of MusicFX </p> <ul><li>Individual Satisfaction rating ( IS ) </li></ul> <ul><li><ul><li>Time i,j= time personjspent listening to genrei </li></ul></li></ul> <ul><li><ul><li>IP i,j= personj s Individual Preference for genrei </li></ul></li></ul> <p> 21. The Goodness of MusicFX </p> <ul><li>Overall Satisfaction rating ( OS ) </li></ul> <ul><li><ul><li>For allNmembers </li></ul></li></ul> <p> 22. Individual Satisfaction for all 275 FX Members 23. The Goodness of the Old Days </p> <ul><li>Three genres ( n=3 ) </li></ul> <ul><li><ul><li>Hottest Hits, Power Hits, Dance </li></ul></li></ul> <ul><li><ul><li>Assume each person listened to each genre 1/3 of the total time spent working out </li></ul></li></ul> <p> 24. Comparing the Old with the New </p> <ul><li>Overall Satisfaction </li></ul> <ul><li><ul><li> Old scheme:0.44 </li></ul></li></ul> <ul><li><ul><li>MusicFX:0.64 </li></ul></li></ul> <ul><li><ul><li><ul><li>8% higher (statistically significant) </li></ul></li></ul></li></ul> <p> 25. Average Individual Preferences 26. Top 10 Stations 27. MusicFX Anecdotes </p> <ul><li>Veto power &amp; IPF </li></ul> <ul><li>Uncommon variety </li></ul> <ul><li>The Polka incident </li></ul> <ul><li>The Chinese Music incident </li></ul> <p> 28. The Future of MusicFX </p> <ul><li>Better awareness of inhabitants </li></ul> <ul><li>Alternative rating/voting schemes </li></ul> <ul><li>Alternative arbitration schemes </li></ul> <p> 29. Individual Satisfaction after 6 months (Avg: 0.64) 30. Average Preferences after 6 months (Avg: -0.39to -0.50) 31. Future Group Preference Applications </p>