1. An Investigation into Guest Movement in the Smart Party Jason Stoops (jstoops@ucla.edu)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 = 2 k 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 ModelHistory LengthThreshold No Movementn/a Randomn/a Threshold Random41 Threshold Least Crowded 41 Threshold Random, Population Weighted 50.5 Threshold Highest Satisfaction 22.25

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/ http://www.audioscrobbler.net/data/webservices/