Thesis belongs to the University of Akron, Ohio, USA.

1. HYBRID RECOMMENDER SYSTEM
ARCHITECTURE FOR PERSONALIZED WELLNESS
MANAGEMENT
Master of Science – Thesis Defense
Prasad Priyadarshana Fernando, Boosabaduge
Complex Engineered Research Lab
Department of Electrical and Computer Engineering
The University of Akron
02/24/2016

2. OUTLINE
 Introduction
 Background
 Hybrid Recommender System Architecture
 Design of the Hybrid Recommender System
 Illustrative Examples and Evaluation
 Conclusion and Future Works
 Demonstration

3. Introduction
• The current healthcare costs in the USA exceeds
$3.8 trillion and the reactive approach to managing
health is not sustainable
• The focus on keeping healthy people healthy is one
approach to addressing this serious societal concern
• Exercising is one of the key requirments for a
healthy life

4. Why Exercising is Important
• Helps prevent diseases
• Improves stamina
• Strengthens and Tones
• Enhances flexibility
• Controls weight
• Improves quality of life

5. Why Exercise Recommendation is needed
• Don’t know what to do
• Don’t know how to do
• Consulting an expert is expensive
• Consulting an expert is time consuming
• Freely available guidelines are not personalized

6. Our Goal
• To empower wellness management at the scale of
the society, efficient software tools are necessary
• So the design objectives for our system were;
– To Reduce the cost burden on participants
– To Improve the access to expertise for the participants
– To explore personalized options for the participant

7. Challenges in Recommending Exercises
• Recommender Systems are widely used to
recommend items such as movies, books, products
and services to users
• The problem of recommending exercises to
participants in a wellness program is, however
challenging and nuanced
• Participants must adhere to the appropriate intensity
and frequency to benefit from the exercise
• These exercises must help the participants to achieve
their wellness objectives

8. Why it is different from Item Recommendation
• The efficacy of the exercises recommended is not
merely the user preference
• The efficacy must often be assessed after a lapse of
several weeks
• Recommended exercises must be refined over a
duration of time
• Health, physical, motivational, occupational, etc.
conditions of a person tend to change as the
particular person uses the recommended exercise

9. The Basis for the Solution
• The participants are expected to interact regularly
with the system by providing their latest
measurement so that the software systems can make
decisions based on the individual's requirements
with the time
• The main concept that was exploited is, the people
with similar physical and motivational conditions
tend to perform similar kind of exercises

10. Proposed Solution
• To address the above challenges, we architected a
hybrid recommender system that incorporates two
major components
– Rule Based Expert System : To evaluate the participant’s
conditions in order to come up with a possible exercise
schedule
– Collaborative Filtering Recommender System : To Explore
more exercises that would be interesing to the participant

11. OUTLINE
 Introduction
 Background
 Hybrid Recommender System Architecture
 Design of the Hybrid Recommender System
 Illustrative Examples and Evaluation
 Conclusion and Future Works
 Demonstration

12. Background
• The background knowledge on the following fields
were useful in building our Hybrid Recommender
System (HyRES) for Exercise Recommendation;
1. Exercise Science Domain Knowledge
2. Rule Based Expert System
3. Recommender System

13. Exercise Science
• In order to integrate exercise science domain knowledge
into HyRES, we used the following overall strategy;
– Determine the participant's capability to exercise based on
physical activities performed recently
– Come up with a suitable exercise duration for the participant
based on the fitness status and the goal of exercising
– Consider the participant's anthropometric and biometric data to
modify the exercise schedule
– Consider the gender, age, diseases and disabilities as high
priorities to alter the recommended intensity and duration
– Estimate the desired daily calorie intake and compare it with the
actual calorie intake to change the exercise schedule
– Explore more exercise based on participants preferences

14. Estimating the Ability to Perform Exercises

15. Evaluating Participants and Exercises
• Metabolic Equivalent of Task (MET) – Exercise Intensity Ratio
• Basal Metabolic Rate (BMR) – Participant’s Daily Calorie Intake
• Body Mass Index (BMI) – Participant’s Obesity

16. Rule Based Expert System
• The exercise science knowledge and practice
guidelines were codified as rules in the Domain
Knowledge Module
• We utilized a Forward Chaining system that started
with the facts and triggered actions or conclusions
based on the Rete Algorithm
• We selected the JBoss Drools platform because it was
open source and supported easy integration with the
Java implementation of HyRES

17. Recommender System
• Recommender Systems use two main techniques to
recommend new items to users;
– Content Based (CB) Recommendation
– Collaborative Filtering (CF) Recommendation
• Starting with the output of the Expert System, the
Recommender System generates more
recommendations by accounting for user preferences
• CF techniques are used in HyRES to find;
– Similar exercises to participant’s preferred exercises
– Similar exercises that similar participants are preferring

18. Collaborative Filtering Approach
• In Collaborative Filtering, we considered two main
approaches
– Neighborhood Method
– Latent Factor Model
• The basis for the CF techniques is the user-exercise
preference matrix
• For Neighborhood based CF, we used pre built set of
algorithms in Apache Mahout Machine Learning
Framework

19. OUTLINE
 Introduction
 Background
 Hybrid Recommender System Architecture
 Design of the Hybrid Recommender System
 Illustrative Examples and Evaluation
 Conclusion and Future Works
 Demonstration

20. HyRES Architecture
• HyRES was organized into the following five modules;
1. PWM Storage Module – Persistent store for all data in
HyRES
2. Participant Dialog Module (PDM) – Gathers participant
details and exerciese preferences
3. Domain Knowledge Module (DKM) – Exercise Science
Domain Knowledge coded in JBoss Drools Expert System
4. Options Exploration Module (EM) – Recommendation
funcitonality supported by Apache Mahout Machine
Learner
5. System Integration Module (IM) – Coordinates the event,
data and control flow across above three modules and
manages the Storage

21. <Interact>
Participant Dialog
Module (PDM)
Participant Detail
Domain Knowledge
Module (DKM)
DKM Prescriptions
Integration Module
(IM)
Rule Execution
Expert Rules
Exercise Selection
Participant
PWM Storage
<Profile>
<Exercises>
Exercise
Recommendation
EM
Recommendations
<Prefer>
Exploration Module (EM)
Collaborative Filtering
Rules.DRL
<Prefer>
<Exercises>
<Participant Profile>
<Recommendations>
Authentication and
Interaction
HyRES Backend Library Architecture

22. Funcitonality of HyRES Components
• Authentication – Authenticates participants and creates
sessions
• Interaction – Coordinates with participant to build the
profile
• Modification – Changes participant’s profile through the
participant dialog
• Storage – Persistant sotrage of participant records
• Rule Execution – Expert system management in DKM
• Exercise Selection – Combines Expert System output with
the exercise data in the PWM Storage to come up with
exercise prescriptions
• Exercise Recommendation – Handles the recommender
system to explore more exercise options for the
participant

23. Integration Module
(IM)
Authentication and
Interaction
Participant
Profile
Exploration
Module (EM)
Domain
Knowledge
Module
(DKM)
Exercise Selection
PWM
Storage
Participant Dialog
Module (PDM)
Stores Participant Data
Obtains Participants Preferences
Builds Participant Profile
Evaluates Participant Profile
Obtains Updated Profile
Obtains ExercisesGenerates Exercise Prescriptions
Processes User Inputs
TriggersExpertSystem
Generates Exercise
Recommendations
Coordinates Participant Dialog
HyRES Component Interaction

24. OUTLINE
 Introduction
 Background
 Hybrid Recommender System Architecture
 Design of the Hybrid Recommender System
 Illustrative Examples and Evaluation
 Conclusion and Future Works
 Demonstration

25. PWM Storage
• Every participant is associated with four data sets;
1. Profile – Data gathered from participant dialog
2. Exercise Prescriptions – Expert System evaluation
3. Exercise Preference – Participant’s Responses for prescribed
exercise
4. Exercise Recommendation – More exercises explored based on
exercise preferences
• PWM Storage supports the above four data models based
on three update rates;
1. Meta Data – These data define the parameters that are used to
specify exercises and participant profiles
2. Static Data – These data do not change after they are entered
into the PWM Store
3. Dynamic Data – These data change frequently, perhaps, with
each new interaction with the participants

26. Participant Profile
(PDM & IM)
Exercise
Prescriptions (DKM)
Exercise Preferences
(PDM & IM)
Exercise
Recommendations
(EM)
Generated through
Participant Dialog Module
(PDM)
Generated by the Domain
Knowledge Module (DKM)
Generated with participant
interaction on DKM decisions
Generated by observing the
participant preference on
DKM decisions
Participant
Evolution of Participant Profile in HyRES

27. Meta Data
exercise types exercise levels exercise goals
participant occupations
Static Data
exercises
participants
participant exercise health
Dynamic Data
participant exercise
preferences
participant general health
administrators
participant activity history
participant exercise
schedules
exercise categories
Multitire Structure of PWM Storage
activity levels

28. Participant Dialog Module (PDM)
• The objective for the Participant Dialog Module is to;
– Gather participant information
– Present potential recommendations
– Collect participant preferences
• To provide participant satisfaction; PDM supports
both Mobile and Web interfaces through a RESTful
service

29. RESTful Web Service
HyRES Backend Library
JSON Interface for Participant Dialog Module (PDM)
Internet <HTTP>
PWM
Storage
Mobile Apps
Web Apps
PDM IM DKM EM
HyRES Interfaces

30. LoginRegister
Rejected
Enter health data
Enter information on exercises
performed during past 7 days
Enter information on the fitness status
Select exercise categories
Express preferences on prescribed
exercises
Get recommendations based on
preferences
Get exercise schedule
Rejected
<Existing Participant><New Participant>
Participant’s Activity Flow

31. Domain Knowledge Module (DKM)
• This module encapsulates the theories from the
domain of exercise science as rules
• JBoss Drools platform supports seamless and robust
interactions between the Java based implementation
of HyRES and Domain Knowledge Module
• The JBoss Drools platform offers a convenient
interface via Java objects
• The main object that is exchanged between the DKM
and HyRES is the Participant Profile

32. Pattern Matcher
(ReteOO)
Drools Rule Engine
Working
Memory
Participant Profile
Domain
Knowledge
(Rules)
Modify
Drools Expert System

33. Participant Attributes Used by DKM Updated by DKM
Gender ✔
Weight (lb) ✔
Height (in) ✔
Age (years) ✔
Blood Pressure ✔
Resting Heart Rate ✔
Daily Calorie Intake ✔
Exercise Goal ✔
Fitness Status ✔
Current Exercise Status ✔ ✔
Basal Metabolic Rate ✔ ✔
Body Mass Index ✔ ✔
Exercise Intensity ✔
Exercise Frequency ✔
Exercise Duration ✔
Participant Object in DKM

34. Drools Rule Structure
• A Rule is made up of two section
– ‘When’ Statement : Condition of the Rule
– ‘Then’ Statement : Action based on the condition
• Within ‘Then’ statment; it can alter the working
memory through modify, retract, update and insert
operation on Java objects in the working memory
• JBoss Drools platform allowes us to set rule priorities
through the ‘Salience’ attribute

35. Sample Exercise Rule in Drools

36. Salience Rule Purpose of Rule
50 Startup Rules Call the rule functions and set the intermediate parameters (BMR, BMI,
etc) in the participants profile
40 IPAQ Exercise
Rules
Set the initial Exercise Intensity based on the Exercise Ability assigned
out of the three exercise levels (Minimal, Moderate and Health
Enhancing)
30 Gender Rule Alter the Exercise Intensity for Male and Female participants
respectively
30 Age Group
Rules
Alter the Exercise Intensity for different age groups
20 Fitness Status
Rules
Override the exercise intensity if the participant is subjected to some
special constraints (disabled, chronic diseases, bedridden)
10 Exercise Goal
Rules
Set the Exercise Frequency and Exercise Duration based on the
participant’s goal of doing exercises
0 BMR Calorie
Rules
Check whether the expressed goal is realistic along with the participant’s
dietary pattern
0 BMI Rules Do any modification to the generated exercise schedule based on the
obesity of the participant
Rules and Priorities in DKM

37. Rule Function Function Purpose
getExerciseLevel Calculate the Exercise Ability of the participant using the answers
given for International Physical Activity Questionnaire (IPAQ)
lifeStyleToActivityLevel Determine the day-to-day activity level of the participant based
on his/her life style. Participant’s occupation is considered as a
key indication of the life style.
getMBICalculation Get the Body Mass Index (BMI) of the participant based on the
anthropometric data in the profile
getBMRCalorieCalculation Calculate the Basal Metabolic Rate (BMR) calorie requirement
for the participant using the Harris Benedict equation
Rule Functions

38. Exploration Module (EM)
• The Exploration Module (EM) in HyRES has been
designed and implemented on the Recommender
System techniques
• We investigated two different CF techniques to design
the EM;
– Matrix Factorization
– Neighborhood Methods
• Participant-Exercise preference matrix is the basis for
those CF techniques
• Neighborhood based CF is the currently used
implementation in HyRES and it was based on the
Apache Mahout ML Framework

39. Participant-Exercise Preference Matrix (M)
Exercises
Participants

40. Matrix Factorization (Latent Factor Model)

41. Stochastic Gradient Descent (SGD) Method
Error: The difference between actual preference and
calculated value
Adjustment to Factor Matrices to minimize the RMSE

42. Neighborhood CF Methods
Pearson Similarity between two participants
Prediction based on a neighborhood

43. Apache Mahout Library Components
Recommender Data Model
Similarity
Neighborhood
PWM
Storage
HyRES
HyRES intercation with Apache Mahout
Components

44. OUTLINE
 Introduction
 Background
 Hybrid Recommender System Architecture
 Design of the Hybrid Recommender System
 Illustrative Examples and Evaluation
 Conclusion and Future Works
 Demonstration

45. Examples and Evaluation
• We used a set of synthetic participants for evaluation
– A synthetic participant is a hypothetical participant that is a
typical representative of an actual participant
• The attributes of the synthetic participants were
selected to test the different values allowed in the
software system
• Decisions made by Expert System and Recommender
System can be compared against the condition and
needs of each participant

46. Age Group Gender Fitness
Status
Exercise Goal Exercise
Ability
Calorie
Intake
BMI
Young (15 <
age < 40)
Male Healthy Stay Healthy Minimal > BMR +
500
> 26
Retired (61 <
age < 90)
Female Disabled Loose Weight Moderate < BMR –
500
< 18
Mid Age (41
< age < 60)
Chronic
Condition
Maintain
after Weight
Loss
Health
Enhancing
Balanced Healthy
Bedridden Prevent
Weight Gain
Attributes of Synthetic Participants

47. User ID Age Gender Weight (lb)
Height
(inch)
Daily Calorie
Intake Fitness Status Occupation Exercise Goal
Vigorous
Exercise
Minutes
Moderate
Exercise
Minutes
Light Exercise
Minutes
u0 28 Male 180 71 2200 Healthy Desk Worker
Prevent Weight
Gain 10 60 280
u1 22 Female 150 64 2000 Healthy Household Loose Weight 20 120 350
u2 36 Female 200 64 2800 Chronic Patient Household Stay Healthy 0 20 140
u3 46 Male 180 73 1800 Healthy Farmer Stay Healthy 120 350 700
u4 16 Male 190 74 2500 Healthy Athlete
Prevent Weight
Gain 90 160 240
u5 26 Female 160 65 2000 Healthy Health Care
Maintain after
Weight Loss 0 160 420
u6 31 Male 200 72 2700 Healthy Academics Loose Weight 10 90 240
u7 56 Female 180 62 2100 Disabled Desk Worker
Prevent Weight
Gain 0 0 180
u8 76 Male 170 73 2000 Bedridden Inactive
Prevent Weight
Gain 0 0 20
u9 30 Female 190 70 2500 Healthy Soldier
Maintain after
Weight Loss 60 200 700
u10 40 Male 175 65 2700 Chronic Patient Porter Loose Weight 20 120 350
u11 26 Male 154 72 2900 Healthy Student Stay Healthy 140 210 420
u12 46 Female 165 66 2000 Bedridden Household Stay Healthy 0 30 75
u13 31 Male 176 60 3000 Healthy Laborer
Prevent Weight
Gain 50 100 420
u14 36 Female 121 60 2000 Healthy Field worker Stay Healthy 20 300 350
u15 38 Female 154 68 2500 Patient Inactive Stay Healthy 0 0 45
u16 25 Male 132 63 3000 Healthy Academics Stay Healthy 105 210 420
u17 71 Female 127 72 3200 Chronic Patient Household
Prevent Weight
Gain 0 25 280
u18 26 Female 110 66 2800 Healthy Student Stay Healthy 210 280 420
u19 22 Male 143 70 2600 Healthy Student Stay Healthy 45 210 300
u20 41 Male 127 72 2000 Disabled Inactive Stay Healthy 0 0 210
u21 26 Male 138 68 3000 Healthy Desk Worker Stay Healthy 300 280 420
u22 31 Female 132 64 2200 Healthy Field worker Stay Healthy 0 50 560
u23 58 Female 119 60 2300 Chronic Patient Household Stay Healthy 45 80 200
u24 26 Male 154 68 2800 Healthy Student Stay Healthy 35 420 420
u25 51 Male 187 72 3000 Disabled Cleaner
Prevent Weight
Gain 20 0 350
Synthetic Participants

48. 0
2
4
6
8
10
12
u0
u1
u2
u3
u4
u5
u6
u7
u8
u9
u10
u11
u12
u13
u14
u15
u16
u17
u18
u19
u20
u21
u22
u23
u24
u25
MaxIntensity(MET)
Participants
Expert System RS (Exercise based Similarity) RS (Participant based Similarity)
Max Intensities Suggested to Synthetic Participants

49. 0
50
100
150
200
250
300
u0
u1
u2
u3
u4
u5
u6
u7
u8
u9
u10
u11
u12
u13
u14
u15
u16
u17
u18
u19
u20
u21
u22
u23
u24
u25
ExerciseMinutesperWeek
Participant
Minutes per Week
Exercise Durations Suggested to Synthetic Participants

50. 0
2
4
6
8
10
12
1 2 3 4 5
RecommendedExerciseIntensity(MET)
IPAQ Exercise Level
Desired Exercise Intensity
Minimal Moderate Health Enhancing
u8, u12
u2, u7, u15
u10
u17, u20, u23, u25
u14
u22
u0, u1, u4, u13,
u16, u19, u24
u5, u6
u18
u3, u9
u11, u21
Exercise Ability vs. Desired Intensity

51. 0
50
100
150
200
250
300
1 2 3 4 5
ExerciseMinutesperWeek
IPAQ Exercise Level
Exercise Duration
Minimal Moderate Health Enhancing
u8, u12
u15
u2
u7
u20
u14, u22, u24
u4, u16, u23
u17, u19
u0, u25
u13
u5
u10
u1
u6
u9
u18, u21
u11
u3
Exercise Ability vs. Exercise Duration

52. OUTLINE
 Introduction
 Background
 Hybrid Recommender System Architecture
 Design of the Hybrid Recommender System
 Illustrative Examples and Evaluation
 Conclusion and Future Works
 Demonstration

53. Conclusion
• We presented a Hybrid Recommender System (HyRES)
that can effectively recommend exercises to participants.
After presenting the overall architecture, the detailed
design of the major modules were also presented.
• The Participant Dialog Module (PDM) gathered
information from the participants based on the guidelines
provided in the well-known IPAQ standard.
• The PWM Storage was designed to store participant
records in a normalized object oriented manner.
• The Domain Knowledge Module (DKM) represented
knowledge from the exercise science domain in rules.
Based on the inputs gathered from the participants, the
DKM rules determined the duration and intensity of
exercises that could be recommended.

54. Conclusion cont.
• The Exploration Module (EM) was designed using a
Collaborative Filtering techniques. Using the output of the
DKM as a basis and participant preferences as input, the
EM identified additional recommendations that could be
recommended to the participants.
• Results based on synthetic participants demonstrated that
DKM rules determined the recommended intensities and
durations based on several factors and EM
recommendations used to align with the decisions made
by DKM.

55. Future Works
• In the future, the rule-set in the DKM must be
expanded to incorporate additional factors and a
rigorous field trial must be conducted to validate the
efficacy of the approach after obtaining appropriate
Institutional Review Board (IRB) clearance.
• For the large scale deployment, the Exploration
Module should be distributed to enhance the
performance; utilizing the support provided through
Mahout Scalable ML Framework.

56. OUTLINE
 Introduction
 Background
 Hybrid Recommender System Architecture
 Design of the Hybrid Recommender System
 Illustrative Examples and Evaluation
 Conclusion and Future Works
 Demonstration

57. Demonstration
• We have developed a sample client application to
demonstrate the HyRES functionality;
– Collects data through user inputs (PDM)
– Triggers Expert System to get initial recommendations
(DKM)
– Call Recommender Systems to explore more
recommendations (EM)
– Presents the summarized exercise schedule to the
participants (IM)
– Store participants records in the persistent store (PWM
Storage)
• Android App developed to work with the RESTful web
service is also available

58. Thank You