Dynamic socialized Gaussian process models for human behavior prediction in a health social network

Yelong Shen, Nhat Hai Phan, Xiao Xiao, Ruoming Jin, Junfeng Sun, Brigitte Piniewski, David Kil, Dejing Dou

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named socialized Gaussian process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals’ behaviors influenced by their friends’ previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual’s behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users’ sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time.

Original languageEnglish (US)
Pages (from-to)455-479
Number of pages25
JournalKnowledge and Information Systems
Volume49
Issue number2
DOIs
StatePublished - Nov 1 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

Keywords

  • Dynamic social correlation
  • Health social network
  • Socialized Gaussian process

Fingerprint

Dive into the research topics of 'Dynamic socialized Gaussian process models for human behavior prediction in a health social network'. Together they form a unique fingerprint.

Cite this