User Location Prediction using MLPs

1. User Location Prediction using MLPs A STUDY ON THE USE OF MULTILAYER PERCEPTRONS TO PREDICT FUTURE LOCATIONS BASED ON PAST LOCATION AND TIME INFORMATION Hans Wegmueller

2. Motivation • My design project – an application that pushes location information to existing social networks, and leverages those social networks to share location information from contacts. • Understanding user location prediction, its limitations and potential, could lead to further services being provided to users, and provide valuable information to advertisers.

3. Past Research • “Mobile User Movement Prediction Using Bayesian Learning for Neural Networks” • Sherif Akoush, American University in Cairo • “A Predictive Location Model for Location-Based Services” • Hassan A. Karimir and Xiong Liu, University of Pittsburgh

4. Dataset • Generated dataset includes location “nodes,” day’s of the week, and time of locations. • Assumes that peoples movements day-to-day follow a pattern, but that the clarity of that pattern differs on a person to person basis. • Dataset generated falls into 4 ‘types’ of people, ranging from someone totally regimented to someone who moves randomly between N nodes each day.

5. Perceptron focus: next node • Build a 3 layer perceptron: • Day of the 14 day cycle mask • Time period mask • Weight most visited locations during day/time period • Found • As one might expect, the more regimented the pattern, the more easy it is to predict next location.

6. Conclusions/Further Study • Perceptron design likely a function of dataset, only real data could determine if the assumptions made during dataset generation yields a useful model for location prediction • Would like to further study designing for variance of time • Then would like to have multiple MLPs that can be chosen between depending on the user, and their past accuracy

7. Questions? • Thank you!