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An Investigation into Guest Movement in the Smart Party

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An Investigation into Guest Movement in the Smart PartyOutlineWhat is the Smart Party?Project MotivationSmart Party Simulation ProgramMetricsKey valuesMobility Models TestedTest ProcedureRound 1 ResultsRound 2: Satisfaction OverviewRound 2: Fairness OverviewTopics for AnalysisMoving Versus Not MovingParty stabilization?Initial room seekingInitial room seeking, cont.Population-based modelsSatisfaction based modelConclusionAcknowledgementsReferencesAn Investigation into Guest Movement in the Smart PartyJason Stoops ([email protected])Faculty advisor: Dr. Peter ReiherOutlineProject IntroductionKey metrics and valuesMobility Models, Methods of TestingResultsAnalysisWhat 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 roomProject 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 satisfactionTest 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 maintainedRound 1 ResultsModel History Length ThresholdNo Movement n/a n/aRandom n/a n/aThreshold Random 4 1Threshold Least Crowded4 1Threshold Random, Population Weighted5 0.5Threshold Highest Satisfaction2 2.25Round 2: Satisfaction Overview18 guests / 3 rooms30 guests / 5 rooms60 guests / 10 rooms90 guests / 15 rooms050100150200250Median Overall Satisfaction25th, 75th quartiles shownNOMOVETHRESHOLD LEAST CROWDEDTHRESHOLD RANDOM POP WEIGHTEDRANDOMTHRESHOLD RANDOMTHRESHOLD HIGHEST SATSatisfactionRound 2: Fairness Overview18 guests / 3 rooms30 guests / 5 rooms60 guests / 10 rooms90 guests / 15 rooms00.050.10.150.20.250.30.350.40.45Median Overall Fairness25th, 75th quartiles shown, lower is betterNOMOVETHRESHOLD LEAST CROWDEDTHRESHOLD RANDOM POP WEIGHTEDRANDOMTHRESHOLD RANDOMTHRESHOLD HIGHEST SATFairnessTopics for AnalysisMoving is better than not movingParty stabilization?Initial room seekingPopulation-based models perform poorlySatisfaction-based model performs wellMoving Versus Not MovingMovement “stirs” party, making previously unavailable songs accessibleSongs users have in common changes with movement, depleted slower.NOMOVE RANDOM020406080100120140160180200Random vs. No Move, Median Overall Satisfaction25th, 75th quartiles shown18 Guests / 3 Rooms30 Guests / 5 Rooms60 Guests / 10 RoomsSatis factio nParty 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 1 0 1 5 2 0 2 5 3 0 3 500 . 10 . 20 . 30 . 40 . 50 . 60 . 70 . 80 . 9R o o m C h a n g e s p e r G u e s t o v e r T i m eT h r e s h o l d H i g h e s t S a t , 3 0 g u e s t s / 5 r o o m sR o u n dM o v e m e n t P r o b a b i l i t yInitial 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 1 0 1 5 2 0 2 5 3 0 3 500 . 10 . 20 . 30 . 40 . 50 . 60 . 70 . 80 . 91R o o m C h a n g e s p e r G u e s t o v e r T i m eT h r e s h o l d H i g h e s t S a t , 6 0 g u e s t s / 1 0 r o o m sR o u n dM o v e m e n t P r o b a b i l i t yInitial room seeking, cont.In satisfaction-based model, peak is in round 2All other models peak in round 1.0 5 1 0 1 5 2 0 2 5 3 0 3 5012345678R o u n d - b y - r o u n d S a t i s f a c t i o n6 0 G u e s t s i n 1 0 R o o m sN O M O V ER A N D O MT H R E S H O L D R A N D O MT H R E S H O L D H I G H E S T S A TR o u n dS a t i s f a c t i o nPopulation-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 rooms020406080100120140160180200Median Overall Satisfaction25th, 75th quartiles shownNOMOVE THRESHOLD LEAST CROWDEDTHRESHOLD RANDOM POP WEIGHTEDRANDOMSa tis factionSatisfaction 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 rooms60 guests / 10 rooms90 guests / 15 rooms050100150200250Median Overall Satisfaction25th, 75th quartiles shownNOMOVE RANDOM THRESHOLD RANDOMTHRESHOLD HIGHEST SATSatis factionConclusion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


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