An Investigation into Guest Movement in the Smart Party

1
An Investigation into Guest Movement in the Smart
Party

• Jason Stoops (jstoops_at_ucla.edu)
• Faculty advisor Dr. Peter Reiher

2
Outline

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

3
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

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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?

5
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.

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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.

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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.

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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

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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

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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
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Round 2 Satisfaction Overview
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Round 2 Fairness Overview
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Topics for Analysis

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

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Moving Versus Not Moving

• Movement stirs party, making previously
  unavailable songs accessible
• Songs users have in common changes with movement,
  depleted slower.

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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.

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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.

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Initial room seeking, cont.

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

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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.

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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.

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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.

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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

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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/