An Investigation into Guest Movement in the Smart PartyJason Stoops (jstoops@ucla.edu)

Faculty advisor: Dr. Peter Reiher

Outline

Project Introduction Key metrics and values Mobility Models, Methods of Testing Results Analysis

What is the Smart Party?

Ubiquitous computing application Someone hosts a gathering Guests bring wireless-enabled devices Devices in the same room cooperate to

select and supply media to be played Songs played in a room represent tastes

of guests present in that room

Project Motivation

Are there ways to move between rooms in the party that can lead to greater satisfaction in terms of music heard?

Can we ultimately recommend a room for the user?

What other interesting tidbits about the Smart Party can we come up with along the way?

Smart Party Simulation Program

Basis for evaluating mobility models (rules of movement).

Real preference data from Last.FM is used. Random subsets of users and songs chosen

Many parties with same conditions are run with different subsets to gather statistics about the party.

Initial challenge: extend existing simulation to support multiple rooms.

Metrics

Satisfaction: based on 0-5 “star” rating Rating determined by play count Exponential scale: k-star rating = 2k satisfaction 0-star rating = 0 satisfaction (song unknown)

Fairness: distribution of satisfaction Gini Coefficient – usually used for measuring

distribution of wealth in a population. In Smart Party, wealth = satisfaction. Ratio between 0 to 1, lower is more fair.

Key values

History Length Number of previously heard songs the user device will

track. Used to evaluate satisfaction with current room

Satisfaction Threshold Used as a guide for when guest should consider

moving. If average satisfaction over last history-length songs

falls below sat-threshold, guest considers moving.

Mobility Models Tested

No movement Random movement Threshold-based random movement Threshold-based to least crowded room Threshold-based, population weighted Threshold-based, highest satisfaction

Test Procedure

Round 1: Broad testing to find good values for history length and satisfaction threshold for each model. (25 iterations)

Round 2: In-depth evaluation of model performance using values found above. (150 iterations)

Ratio of six guests per room maintained

Round 1 Results

Model History Length Threshold

No Movement n/a n/a

Random n/a n/a

Threshold Random 4 1

Threshold Least Crowded

4 1

Threshold Random, Population Weighted

5 0.5

Threshold Highest Satisfaction

2 2.25

Round 2: Satisfaction Overview

18 guests / 3 rooms30 guests / 5 rooms

60 guests / 10 rooms90 guests / 15 rooms

0

50

100

150

200

250

Median Overall Satisfaction

25th, 75th quartiles shown

NOMOVE

THRESHOLD LEAST CROWDED

THRESHOLD RANDOM POP WEIGHTED

RANDOM

THRESHOLD RANDOM

THRESHOLD HIGHEST SATSa

tisfa

ctio

n

Round 2: Fairness Overview

18 guests / 3 rooms30 guests / 5 rooms

60 guests / 10 rooms90 guests / 15 rooms

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Median Overall Fairness

25th, 75th quartiles shown, lower is better

NOMOVE

THRESHOLD LEAST CROWDED

THRESHOLD RANDOM POP WEIGHTED

RANDOM

THRESHOLD RANDOM

THRESHOLD HIGHEST SAT

Fa

irn

es

s

Topics for Analysis

Moving is better than not moving Party stabilization? Initial room seeking Population-based models perform poorly Satisfaction-based model performs well

Moving Versus Not Moving

Movement “stirs” party, making previously unavailable songs accessible

Songs users have in common changes with movement, depleted slower. NOMOVE RANDOM

0

20

40

60

80

100

120

140

160

180

200

Random vs. No Move, Median Overall Satisfaction

25th, 75th quartiles shown

18 Guests / 3 Rooms

30 Guests / 5 Rooms

60 Guests / 10 Rooms

Sa

tis

factio

n

Party stabilization?

Do users find “ideal rooms” and stop moving?

No! Some movement is always occurring.

Cause: Preferences are not static, they evolve over time.

0 5 10 15 20 25 30 35

0

0.1

0 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

0 .9

R o o m C hang e s p e r G ue s t o ve r T im eThres hold Highes t S at, 30 gues ts / 5 room s

Round

Mov

emen

t P

roba

bilit

y

Initial room seeking

90% of guests move after round 1

Guests have some information to go on after one song plays.

Guests that like the first song in a room likely have other songs in common.

0 5 10 15 20 25 30 35

0

0.1

0 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

0 .9

1

R oo m C hanges pe r G ues t ove r T im eThresho ld Highest S at, 60 guests / 10 rooms

Round

Mov

emen

t P

roba

bilit

y

Initial room seeking, cont.

In satisfaction-based model, peak is in round 2 All other models peak in round 1.

0 5 10 15 20 25 30 35

0

1

2

3

4

5

6

7

8

Round-by -round S at is fac t ion

60 Gues ts in 10 Rooms

N O M O VE

R AN D O M

T H R ESH O LD R AN D O M

T H R ESH O LD H IG H EST SAT

R ound

Sa

tis

fac

tio

n

Population-based models

Worse than choosing a room at random!

Weighted model performed better as weighting approached being truly random.

However, still better than not moving at all.

18 guests / 3 rooms 30 guests / 5 rooms

0

20

40

60

80

100

120

140

160

180

200

Median Overall Satisfaction

25th, 75th quartiles shown

NOMOVE THRESHOLD LEAST CROWDED

THRESHOLD RANDOM POP WEIGHTED

RANDOM

Sa

tis

factio

n

Satisfaction based model

Informed movement better than random movement.

Greater advantage as more rooms are added.

Short history length (two songs) used since history goes “stale”.

18 guests / 3 rooms30 guests / 5 rooms

60 guests / 10 rooms90 guests / 15 rooms

0

50

100

150

200

250

Median Overall Satisfaction

25th, 75th quartiles shown

NOMOVE RANDOM THRESHOLD RANDOM

THRESHOLD HIGHEST SAT

Sa

tis

factio

n

Conclusion

Room recommendations are a feasible addition to the Smart Party User Device Application.

Recommendations based on songs played are more valuable than those based on room populations.

Movement is a key part of the Smart Party.

Acknowledgements

At the UCLA Laboratory for Advanced Systems Research: Dr. Peter Reiher Kevin Eustice Venkatraman Ramakrishna Nam Nguyen

For putting together the UCLA CS Undergraduate Research Program Dr. Amit Sahai Vipul Goyal

References

Eustice, Kevin; Ramakrishna, V.; Nguyen, Nam; Reiher, Peter, "The Smart Party: A Personalized Location-Aware Multimedia Experience," Consumer Communications and Networking Conference, 2008. CCNC 2008. 5th IEEE , vol., no., pp.873-877, 10-12 Jan. 2008

Kevin Eustice, Leonard Kleinrock, Shane Markstrum, Gerald Popek, Venkatraman Ramakrishna, Peter Reiher . Enabling Secure Ubiquitous Interactions, In the proceedings of the 1st International Workshop on Middleware for Pervasive and Ad-Hoc Computing (Co-located with Middleware 2003), 17 June 2003 in Rio de Janeiro, Brazil.

Gini, Corrado (1912). "Variabilità e mutabilità" Reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi (1955).

Audioscrobbler. Web Services described at http://www.audioscrobbler.net/data/webservices/