An Investigation into Guest Movement in the Smart Party

1. An Investigation into Guest Movement in the Smart Party Jason Stoops (jstoops@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

4. 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.

6. 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.

7. 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.

8. 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

9. 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

10. Round 1 Results

11. Round 2: Satisfaction Overview

12. Round 2: Fairness Overview

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

14. Moving Versus Not Moving • Movement “stirs” party, making previously unavailable songs accessible • Songs users have in common changes with movement, depleted slower.

15. 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.

16. 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.

17. Initial room seeking, cont. • In satisfaction-based model, peak is in round 2 • All other models peak in round 1.

18. 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.

19. 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”.

20. 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.

21. 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

22. 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/