Interaction network representations for human behavior prediction

Amnay Amimeur, Nhathai Phan, Dejing Dou, David Kil, Brigitte Piniewski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Human behavior prediction is critical to studying how healthy behavior can spread through a social network. In this work we present a novel user representation based human behavior prediction model, the User Representation-based Socialized Gaussian Process model (UrSGP). First, we present the Deep Interaction Representation Learning (Deep Interaction) model for learning latent representations of interaction social networks in which each user is characterized by a set of attributes. In particular, we consider social interaction factors and user attribute factors to build a bimodal, fixed representation of each user in the network. Our model aims to capture the evolution of social interactions and user attributes and learn the hidden correlations between them. We then use our latent features for human behavior prediction via the UrSGP model. An empirical experiment conducted on a real health social network demonstrates that our model outperforms baseline approaches for human behavior prediction.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages87-93
Number of pages7
ISBN (Electronic)9781509061662
DOIs
StatePublished - Jan 31 2017
Externally publishedYes
Event15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States
Duration: Dec 18 2016Dec 20 2016

Publication series

NameProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

Other

Other15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
CountryUnited States
CityAnaheim
Period12/18/1612/20/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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