MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts Joe McCarthy Ted Anagnost Andersen Consulting Center for Strategic Technology Research

Outline UbiComp & Intelligent Environments The MusicFX System Evaluating Group Preference Arbitration Future Work

UbiComp & Intelligent Environments

The MusicFX System

Evaluating Group Preference Arbitration

Future Work

Ubiquitous Computing Proliferation of networked devices phones, TVs, cam's, mic's, microwaves, refrigerators... Distribution of computing resources portable, wearable, embedded New paradigm of computing input/output ( foreground ) --> sense/respond ( background )

Proliferation of networked devices

phones, TVs, cam's, mic's, microwaves, refrigerators...

Distribution of computing resources

portable, wearable, embedded

New paradigm of computing

input/output ( foreground ) --> sense/respond ( background )

Intelligent Environments “ UbiComp in a box” Interconnected, cooperating devices Concentrated in a small area (e.g., one room) Redefining HCI users --> inhabitants

“ UbiComp in a box”

Interconnected, cooperating devices

Concentrated in a small area (e.g., one room)

Redefining HCI

users --> inhabitants

Observation 1 Most UbiComp applications focus on single individuals in multiple spaces Active Badge: open doors, teleporting ParcTab: information access, email What about multiple inhabitants in a single, shared space ?

Most UbiComp applications focus on single individuals in multiple spaces

Active Badge: open doors, teleporting

ParcTab: information access, email

What about multiple inhabitants in a single, shared space ?

Observation 2

Music in the Fitness Center (FX) Popular in the Complaint Department 25% of “feedback” focused on music RSI: Repetitive Song Injury 3 stations played, 91 available (DMX) Squeaky Wheels Vocal minority prevails over silent majority Hangovers This morning’s music = last night’s music

Popular in the Complaint Department

25% of “feedback” focused on music

RSI: Repetitive Song Injury

3 stations played, 91 available (DMX)

Squeaky Wheels

Vocal minority prevails over silent majority

Hangovers

This morning’s music = last night’s music

Four Issues for any Intelligent Environment Who’s here? What are they doing? What are their preferences? What can I do to help?

Who’s here?

What are they doing?

What are their preferences?

What can I do to help?

Four Issues for MusicFX Who’s here? Members who login [badge reader] What are they doing? Working out while listening to music What are their preferences? Diverse (to say the least) What can I do to help? Play “good” music

Who’s here?

Members who login [badge reader]

What are they doing?

Working out while listening to music

What are their preferences?

Diverse (to say the least)

What can I do to help?

Play “good” music

The MusicFX System Database of musical preferences Group Preference Arbitration algorithm Group Preference Calculation Candidate Identification Weighted Random Selection operator

Database of musical preferences

Group Preference Arbitration algorithm

Group Preference Calculation

Candidate Identification

Weighted Random Selection operator

Music Preference Database 275 fitness center members 91 musical genres (DMX stations) 5-point rating scale +2 = I love this music +1 = I like this music 0 = I don’t mind this music -1 = I dislike this music -2 = I hate this music

275 fitness center members

91 musical genres (DMX stations)

5-point rating scale

+2 = I love this music

+1 = I like this music

0 = I don’t mind this music

-1 = I dislike this music

-2 = I hate this music

Group Preference Arbitration Group Preference Calculation Candidate Identification Weighted Random Selection

Group Preference Calculation

Candidate Identification

Weighted Random Selection

Group Preference Calculation Where GP i = G roup P reference for genre i IP i,j = I ndividual P reference of person j for genre i N = N umber of inhabitants

Where

GP i = G roup P reference for genre i

IP i,j = I ndividual P reference of person j for genre i

N = N umber of inhabitants

Candidate Identification Sort genre list by GP i Remove any undesireable genre Individual Preference Filter Candidates are the first M genre Group Preference Filter

Sort genre list by GP i

Remove any undesireable genre

Individual Preference Filter

Candidates are the first M genre

Group Preference Filter

Weighted Random Selection Calculate weights for candidates Probabilistically select genre according to W i

Calculate weights for candidates

Probabilistically select genre according to W i

An example

Environmental Events Member entrance Login (badge reader) Member exit Timeout (90 minutes) Individual Preference Update System Parameter Adjustment Individual / Group Preference Filter, Maximum Play Time Maximum Play Time Elapsed

Member entrance

Login (badge reader)

Member exit

Timeout (90 minutes)

Individual Preference Update

System Parameter Adjustment

Individual / Group Preference Filter, Maximum Play Time

Maximum Play Time Elapsed

The Success of MusicFX Daily operation since November 1997 Poll results (after 6 weeks)  : increased variety, having some influence  : abrupt changes, occasional “bad” music

Daily operation since November 1997

Poll results (after 6 weeks)

 : increased variety, having some influence

 : abrupt changes, occasional “bad” music

Evaluating Group Preference Arbitration Calculate the “goodness” of MusicFX Estimate the “goodness” of old scheme Compare the old with the new

Calculate the “goodness” of MusicFX

Estimate the “goodness” of old scheme

Compare the old with the new

The “Goodness” of MusicFX Individual Satisfaction rating ( IS ) Time i,j = time person j spent listening to genre i IP i,j = person j ’s Individual Preference for genre i

Individual Satisfaction rating ( IS )

Time i,j = time person j spent listening to genre i

IP i,j = person j ’s Individual Preference for genre i

The “Goodness” of MusicFX Overall Satisfaction rating ( OS ) For all N members

Overall Satisfaction rating ( OS )

For all N members

Individual Satisfaction for all 275 FX Members

The “Goodness” of the Old Days Three genres ( n=3 ) Hottest Hits, Power Hits, Dance Assume each person listened to each genre 1/3 of the total time spent working out

Three genres ( n=3 )

Hottest Hits, Power Hits, Dance

Assume each person listened to each genre 1/3 of the total time spent working out

Comparing the Old with the New Overall Satisfaction “ Old scheme”: 0.44 MusicFX: 0.64 8% higher (statistically significant)

Overall Satisfaction

“ Old scheme”: 0.44

MusicFX: 0.64

8% higher (statistically significant)

Average Individual Preferences

Top 10 Stations

MusicFX Anecdotes Veto power & IPF Uncommon variety The Polka incident The Chinese Music incident

Veto power & IPF

Uncommon variety

The Polka incident

The Chinese Music incident

The Future of MusicFX Better awareness of inhabitants Alternative rating/voting schemes Alternative arbitration schemes

Better awareness of inhabitants

Alternative rating/voting schemes

Alternative arbitration schemes

Individual Satisfaction after 6 months (Avg: 0.64)

Average Preferences after 6 months (Avg: -0.39 to -0.50)

Future Group Preference Applications