This document discusses recommender systems for personalized health education. It begins with an introduction to health education and tailored health education. It then discusses the potential of recommender systems to help address information overload on the web for health information. The main types of recommender systems are described as collaborative, content-based, and hybrid. Examples of existing health recommender systems are provided. Opportunities and challenges of using recommender systems for health education are outlined. In conclusions, it is noted that recommender systems have potential to help access relevant health education resources but also differ from traditional recommender system scenarios.

1. Introduction Recommender Systems for Health Conclusions
Challenges and Opportunities of using
Recommender Systems for Personalized
Health Education
Luis Fernandez-Luque*1 Randi Karlsen12 Lars K. Vognild1
1
Northern Research Institute (Norut), Tromso, Norway
2
Computer Science Department, University of Tromso, Tromso, Norway
Medical Informatics Europe, MIE 2009, 2nd September 2009

2. Introduction Recommender Systems for Health Conclusions
INTRODUCTION
Health Education
Health Education
Education that increases the awareness and favorably influences
the attitudes and knowledge relating to the improvement of
health on a personal or community basis. (WHO)
Tailored Health Education
The adaptation of health education to one specific person
through a largely computerized process a
a Hein de Vries et al., Computer-tailored interventions motivating people to adopt health
promoting behaviors: Introduction to a new approach, Patient Education and Counseling

3. Introduction Recommender Systems for Health Conclusions
INTRODUCTION
Tailored Health Education
Designed for a specific disease or attitude, based on human
experts and theoretical models.1
Tailoring/Personalization has three main elements:
User modeling: gathering patient information (e.g. standardized
questionnaires or EHR)
Document modeling: description of educational resources (e.g.
manual techniques)
Tailoring algorithm: selection and modification of resources,
based on expert rules
Channel: delivery of tailored resources (e.g. email, post, web,
SMS, etc.)
1 Lustria ML et al., Computer-tailored health interventions delivered over the Web: review and
analysis of key components., Patient Education and Counseling

4. Introduction Recommender Systems for Health Conclusions
INTRODUCTION
Health Education in the Web 2.0
Most of the population uses the Internet to access health
information
Many types of resources: videos, blogs, images, forums,
flash-tutorials, etc.
Published by hospitals, medical associations, patients,
government, etc.
Also, it is difficult to find good content: autopsy pictures, herbal
cures for cancer, etc.

5. Introduction Recommender Systems for Health Conclusions
INTRODUCTION
Information Overload
The web search space is so huge (Information Overload) that
Information Filtering techniques are needed, such as Search Engines
(e.g. Google) and Recommender Systems (e.g. Amazon
Recommendations).

6. Introduction Recommender Systems for Health Conclusions
INTRODUCTION
Information Overload
The web search space is so huge (Information Overload) that
Information Filtering techniques are needed, such as Search Engines
(e.g. Google) and Recommender Systems (e.g. Amazon
Recommendations).

7. Introduction Recommender Systems for Health Conclusions
RECOMMENDER SYSTEMS
What is a Recommender System?
Recommender System
Recommender systems form a specific type of Information Filtering
(IF) technique that attempts to present information items (e.g.
movies, music, books, news, images, web pages, etc.) that are likely
of interest to the user. (Wikipedia)

8. Introduction Recommender Systems for Health Conclusions
RECOMMENDER SYSTEMS
Main types of Recommender Systems
There are three main types of Recommender Systems:
Collaborative: based on knowledge gathered from users
Content: based on knowledge gathered from the users and item
descriptions
Hybrid: a combination of different techniques
Collaborative Recommender System
1 User modeling: previous interactions and user ratings
2 Doc modeling: the collection of ratings from different users
3 Algorithm: recommendations are based on data collected from a
particular user’s neighborhood (e.g. "people like you like")
Problems and advantages: there is no need to describe items (+)
and recommendations are not repetitive (+), yet performance is low
with new users and items (e.g. cold start problem) (-)

9. Introduction Recommender Systems for Health Conclusions
RECOMMENDER SYSTEMS
Main types of Recommender Systems
There are three main types of Recommender Systems:
Collaborative: based on knowledge gathered from users
Content: based on knowledge gathered from the users and item
descriptions
Hybrid: a combination of different techniques
Content-based Recommender System
1 User modeling: previous interactions and user ratings
2 Doc modeling: item characteristics.
3 Algorithm: recommendations are based on data collected from
previous user interactions (e.g. "these items are similar to what
you liked before")
Problems and advantages: items need to be described (-),
recommendations can be repetitive (-), there is no need to have a
critical mass of users (+), low performance with new users (-)

10. Introduction Recommender Systems for Health Conclusions
EXAMPLES
Examples of Recommender Systems for Health
HealthyHarlem: tag-based recommender system that suggests
online resources in a health promotion community (Khan SA,
University of Columbia, USA)
Cancer Sites Recommender: usage of collaborative and
content-based techniques to recommend prostate cancer webs
(Witteman H, University of Toronto, Canada)
Suggestion systems for educational resources while navigating
patient records.
MyHealthEducator: an ongoing project where video
recommendations are based on collaborative techniques and a
Personal Health Record (L. Fernandez-Luque, Norut, Norway)

11. Introduction Recommender Systems for Health Conclusions
CHALLENGES AND OPPORTUNITIES
Recommender Systems for Health Education: Opportunities
Recommender Systems need less expert involvement due to
automatic and collaborative techniques.
Integration with Personal Health Records (PHR) can improve
recommendations and reduce the cold start problem.
Collaborative techniques gather aspects such as user
preferences, which are not very common in health education
tailoring.
Automatic analysis of User-Generated Content for modeling
users (e.g. Risbot) or modeling documents (e.g. HealthyHarlem)
can improve recommendations and increase knowledge about
the users
The knowledge of the human experts about tailoring health
education can improve health recommender systems.

12. Introduction Recommender Systems for Health Conclusions
CHALLENGES AND OPPORTUNITIES
Recommender Systems for Health Education: Challenges
Recommender Systems can be attacked by users (e.g. to
promote a certain document)
Most Recommender Systems are based on popularity and thus
may not lead to good resources (e.g. proanorexia videos are
popular)
Integration between web health applications is not yet prominent
Web data mining for user modeling has ethical implications (e.g.
should we model race, gender, sexual orientation?)

13. Introduction Recommender Systems for Health Conclusions
CONCLUSIONS
Conclusions
It is difficult to find web educational resources due to Information
Overload.
Recommender Systems have the potential to facilitate access to
relevant educational resources since they are designed for the
context of Information Overload.
Health Education differs a lot from the traditional scenarios of
Recommender Systems
A lot of user information (if integrated with a PHR)
Health Education can not be only based on popularity.
Resources need to be quality controlled.

14. Introduction Recommender Systems for Health Conclusions
CONCLUSIONS
Thank you
Luis Fernandez-Luque (luis.luque@norut.no)