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American Family Insurance, Madison, Wisconsin – 13th July, 2015
Social RBM Model for Human Behavior Modeling in
Health Social NetworksNhatHai Phan
CIS Dept., University of [email protected]
https://sites.google.com/site/ihaiphan/
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Outline
• YesiWell Health Social Network • Motivation• Human Behavior Modeling– Prediction – Explanation
• Conclusions
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Obesity & Physical Activity Interventions
• 18 states (30% - 35%), 2 states (>= 35%)• Treatment cost:
– $147 billions (in 2008)
• 30 minutes, 5 days• Interventions
– Telephone (16)– Website (15)– Effective in short term (<3months)
Prevalence* of Self-Reported Obesity Among U.S. Adults
CDC, http://www.cdc.gov/obesity/data/prevalence-maps.html-2014E.G. Eakin et al. 2007 C. Vadelanotte et al. 2007
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YesiWell Health Social Network
• 254 Overweight and Obese individuals in the YesiWell study
• Daily physical activities– Walking, running, jogging,
distance, speed, intensity, …
• Social activities– Online social network, text
messages, posts, comments, …– Social games, competitions, …
• Biomarkers, biometric measures – Cholesterol, triglyceride, BMI, …
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Impact of Online Social Network
• Increase weekly leisure walking from 129 to 341 minutes, on average, a 164% increase over the 6-month study period, compared with a 47% increase for the control group.
Motivation
• Understanding influence of healthcare social networks, such as YesiWell, on sustained weight loss.
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Health Social Network
Prediction Explanation
Physical Activity Propagation
Textual Understanding
Privacy Preserving
Prediction Explanation
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Outline
• YesiWell Health Social Network • Motivation• Human Behavior Modeling– Prediction – Explanation
• Conclusions
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Human Behavior Prediction
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t1t 1tDecrease exercise Increase exercise
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Challenging Issues
Baseline approaches• Logistic Auto-Regression (LAR),
Gaussian Process Model (GP), Behavior Pattern Search (BPS), Conditional RBM (CRBM), Socialized Gaussian Process (SGP), Socialized Logistic Auto-Regression (SLAR), Socialized ctRBM (SctRBM)
Challenges• Unobserved social relationship• Evolving of the social network
– Temporal effects• (Explicit & implicit) Social influences• Interaction of human behavior
determinants
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Human Behavior Determinants
• Self-motivation:– Personal ability, Attitudes,Intentions, Effort, Withdrawal
• Social influence:– Explicit: friends – Implicit: colleagues, social
context, etc.
• Environmental events:– #competitions, #social
games, #meet-ups
• 33 features to model human behavior
Human Behavior
Self-motivation
Environmentalevents
Social Influence
A. Bandura, “Human agency in social cognitive theory,” The American Psychologist, 1989.N. Christakis, “The hidden influence of social networks,” in TED, 2010.
Human agency in social cognitive theory
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Social Restricted Boltzmann Machine (SRBM)
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Self-Motivation
Implicit Social Influence
Environmental Events
Explicit Social Influence
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Explicit Social Influence
• Homophily principle– “love the same”
• Physical activity-based social Influence
N. Phan, D. Dou, X. Xiao, B. Piniewski, and D. Kil, “Analysis of physical activity propagation in a health social network,” in CIKM’14, pp. 1329–1338.
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Training SRBM
• Minimize energy function
• Softmax layer for human behavior prediction
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Experimental Setting33 features1,371 connections2,766 messages11,458 competitionsM = 10 weeks N = 3 weeksCD5
initial weights = N(0, 0.01)iterations = 1,000learning rate = 0.01 weight decay = 0.01#hidden units = 100
Competitve Models Shallow Deep
LAR, GP, BPS, SGP, SLAR o
CRBM, SctRBM o
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Experimental Results• Prediction accuracy
• SRBM : 88.7%• State-of-the-art: 75.21%
• 1 layer vs 2 layers
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Improve Local Representation Learning
• Flat setting of individual features– Local representations are not good enough
• Sparsity and transition of social influences– Convolutional deep belief networks – Temporal effects of the past?
• Feature clustering– PCA
• Structural designs– Ontology
Lee et al., 2009; Desjardins and Bengio, 2008; Norouzi et al., 2009.
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Ontology-based SRBM (HuBex)
• 5 layers in total
A BConcept Sub-concept
AConcept
Feature
Relation
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Validation of the HuBex model for Prediction
• Validation of functions and components
• Ontology impact: 3.22%• Optimal setting impact: 12%
• Human behavior prediction
• HuBex: 91.92%• SRBM : 88.7%• State-of-the-art: 75.21%
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Outline
• YesiWell Health Social Network • Motivation• Human Behavior Modeling– Prediction – Analysis of Human Behavior Determinants and
Explanation• Conclusions
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Analysis of Human Behavior Determinants
• Quantitative effects of human behavior determinants– – self-motivation, implicit social influence, explicit
social influence, environmental events
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Explanation Generation
• Quantitative effects of human behavior determinants–
• Apply interpretable classifiers on top of the Hubex model
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Outline
• YesiWell Health Social Network • Motivation• Human Behavior Modeling– Prediction – Explanation
• Conclusions
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Conclusions
• Prediction & Explanation– HuBex: 91.92%– SRBM: 88.7%– Explanation: 86.82%– ASONAM’15, ACM BCB’15
(Under review ICDM’15)
• Privacy Preserving– Deep private Auto-encoders– Human behavior prediction:
83.392%(Under review ICDM’15)
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American Family Insurance, Madison, Wisconsin – 13th July, 2015
[email protected]://sites.google.com/site/ihaiphan/
YesiWell Health Social Network
Thank you!
