1 / 7

User Location Prediction using MLPs

This study explores the prediction of user locations using Multilayer Perceptrons (MLPs). It focuses on an application designed to leverage social networks for locating users based on historical movement patterns. By analyzing a generated dataset comprising location nodes, days, and times, the research highlights varying movement patterns among users. The study develops a 3-layer perceptron to predict the next location, revealing that more regimented behaviors yield better predictive accuracy. Future work aims to refine MLP design for variability in time and adapt models based on user history.

sophie
Download Presentation

User Location Prediction using MLPs

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related