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1. The 4th
IEEE International Conference on E-Health and Bioengineering - EHB 2013
Grigore T. Popa University of Medicine and Pharmacy, Iaşi, Romania, November 21-23, 2013
978-1-4799-2373-1/13/$31.00 ©2013 IEEE
A Systematic Literature Review on Health
Recommender Systems
Emre Sezgin
Informatics Institute
Middle East Technical University
Ankara, Turkey
esezgin@metu.edu.tr
Sevgi Özkan
Informatics Institute
Middle East Technical University
Ankara, Turkey
sevgiozk@metu.edu.tr
Abstract—Health Information Systems are becoming an
important platform for healthcare services. In this context,
Health Recommender Systems (HRS) are presented as
complementary tools in decision making processes in healthcare
services. Health Recommender Systems increase usability of
technologies and reduce information overload in processes. In
this paper, a literature review was conducted by following a
review procedure. Major approaches in HRS were outlined and
findings were discussed. The paper presented current
developments in the market, challenges and opportunities
regarding to HRS and emerging approaches. It is believed that
this study is an illuminating start-up point for HRS literature
review.
Keywords— Health Recommender Systems; Health
Information Systems; Literature review
I. INTRODUCTION
Today, information technologies have led to number of
innovations and developments in number of fields. In this
context, Recommender systems (RS) have been a cutting
edge development in the service industry [1]. In the case of
web-based services, RS aims to increase reachability of
products and to provide alternatives for potential customers.
Many variances of RS have been used in online stores (such
as eBayTM and AmazonTM), and it is substantially being
adapted by many organizations on the web. However, RS is
not limited to marketing products online. On the other side,
RS serve to decision support mechanism by providing options
(substitutes) to decision makers [2, 3]. In health services,
information systems have assisted to optimize decision
making processes and to increase effectiveness of
communication channels and infrastructures, such as ERP
systems. In the health industry, RS has a significant role in
terms of assisting decision making processes about
individuals’ health. The studies demonstrated that RS have
already been employed in health services, as Health
Recommender Systems (HRS), for educational, dietary, and
activity assistance purposes [4-7]. However, review study of
Park et. al. [3] and literature research of scholar databases
unveiled that relevant studies are rare in the field. The
literature has presented several researches about RS being
used in health information services.
On the other side, literature researches also showed that
there is no trace or initiation about a review of studies and
practices in HRS. It is an acceptable outcome in the field of
HRS in which resources are limited. However, it is important
to introduce a set of knowledge for researchers who are
interested in HRS studies. Thus, here, this paper provides a
preliminary literature review study in HRS domain. The
literature was reviewed systematically and findings were
presented considering the purpose of HRS and methods. In
the following sub-sections, RS and HRS will be introduced.
Then, methodology of the review, discussion of findings and
conclusion sections were outlined.
A. Recommender Systems
Due to “big data” piling up on the web, recommender
systems have gained importance with respect to data
cleansing and mining. In the early years of 90s, it was
identified that information filtering techniques were needed in
order to retrieve the information effectively. It was defined as
“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”. In the literature,
there are fundamentally three types of filtering in
Recommender Systems [2]:
• Collaborative Filtering: It is based on the knowledge
collected and composed from users. Example: AmazonTM
• Content-based Filtering: It is based on the
knowledge aggregated from the users and unit descriptions of
historical data. For example: Last.fmTM
• Hybrid Filtering: It is a combination of different
approaches and techniques, basically combining collaborative
and content based filtering.
The Appendix A exhibited the filtering types, their
definitions, primary points of pros and cons and examples of
Recommender Systems. In addition to 3 fundamental filtering
types, two emerging types of filtering, knowledge-based and
mobile RS, were also presented in the Appendix A. These
filtering methods were identified as promising in HRS
domain due to their incremental approaches and additional
dataset involvement in recommending methods.

2. B. Health Recommender Systems
Health Recommender Systems are part of RS being applied
in the health industry. It has been used for diagnostic
assistance by physicians and for personal health advising
tools by users [2]. As the communication platform, Internet
has been the main source for users to access health
information and recommendations. Fernandez et al. classified
the health information being searched on the internet as
following [1]:
• Image, videos, web blogs, forums, tutorials, etc.
• Publications by medical organizations, patients,
governments, etc.
• Multimedia resources on autopsies, recipes on herbal
cures for cancer, etc.
Thus, HRS have significant role in filtering information for
self-diagnostic searches of users on the web as well as the
given categories. In addition to that, HRS have been used by
physicians for diagnostic and educational purposes. In this
manner, suggesting online health resources (HealthyHarlem),
cancer related web sites and educational resources with
patient records (MyHealthEducator) can be given as
examples for web based diagnostic recommender system use
[1].
C. Issues of Recommender Systems
Aside from common filtering problems (Sparsity, Cold
start and Scalability problems), it is important to point out a
major socio-technical issue about RS. Privacy is the major
and emerging topic in this context. Using data from multiple
sources may raise a question about use of individual private
data. This issue was identified [9] as “the combination of
weak ties (an unexpected connection that provides
unexpected recommendations) and other data sources can be
used to uncover identities of users in an anonymized dataset”
Thus, it may present an important flaw especially in health
information, which constitutes a delicate topic about privacy.
II. LITERATURE REVIEW METHODOLOGY
Since HRS are an emerging and a new field of research, the
database research demonstrated that new studies were
mostly presented on conferences, instead of top scientific
journals. Many papers on the field can be found on journals
of conference proceedings. Thus, in this study, research
criteria were expanded considering keeping the quality of
studies high but covering publications in conference
proceedings.
The research method was built upon a review protocol
in order to review the literature systematically. In the study,
Kithchenam’s systematic review procedure was employed
[10]. The following steps were pursued:
1. Determining the topic of the research
2. Extraction of the studies from literature considering
exclusion and inclusion criteria
3. Evaluation of the quality of the studies
4. Analysis of the data
5. Report of the results
Fig 1. Literature Review process
The process of literature review (Fig. 1) was started with
research of leading academic databases (Sciencedirect,
IEEE and Scopus) about HRS and its practice. The
keywords were composed of “health”, “recommendation”,
“system”, “recommender”, “eHealth”. Initial refinement
was made considering 2 criteria: Publication year (within 10
years: 2002-2012) and quality of journals (by evaluating
impact factor and citation rates). Then, the studies were
retrieved and refined by title and abstract basis. In total, 310
papers were retrieved. It was refined to 251 papers by title-
basis elimination and to 35 papers by abstract-basis
elimination. In the next phase, 8 papers were found meeting
the following criteria of quality: reliability of the source,
integrity in the content and providing applicable studies. In
the final phase, findings were synthesized and reported.
Method and Discussion sections of papers were the
main focus area in analysis. In order to acquire information
about aim and methodology of the studies, key elements in
each section of papers were noted. Socio-technical aspects
were held primary point of research rather than pure
technical side of studies in order to provide a generic body
of knowledge about all of the studies.
There have been several limitations while conducting
the study. First, the scarcity of academic resources in HRS
was the main limitation. In addition to that, the studies were
not explicitly providing details about their methods and
techniques, and their variety of research approaches
disallowed to make classification of HRS studies. Thus,
they were presented “as it is” in the following section.
III. FINDINGS AND DISCUSSION
In total, 8 papers were found likely to contribute to HRS
review. The list of papers, their aims and methods being
employed were given in Appendix B.
The review results generically presented that HRS were
analyzed in terms of user groups and system design [5, 7, 8,
11], and a set of studies aimed to investigate physical
activities and nutrition based recommender systems [4, 6,
12]. In addition to that, two of the studies aimed to outline
challenges and opportunities [1, 12]. Electronic health
records were also part of HRS in terms of health marketing,
personal recommendations and self-examination [5, 7, 11].
Trending domain in HRS was pinpointed as telemedicine
[8]. The current studies showed that telemedicine and
diagnosis applications were main target of HRS studies in
terms of managing health affairs for housebound and
mobile patients.
In HRS, it should be underlined that semantics is also a
challenging topic which is important input in predicting
user behavior on the web [13]. Thus, papers put emphasis

3. TABLE 1
CHALLENGES AND OPPORTUNITIES IN HRS
Challenges Opportunities
• HRS can be a target of cyber-
attacks due to its significance
• Generally RS based on
popularity of resources, thus
it may be misleading in HRS
• Web health applications have
not been yet capable of
integration in terms of data
exchange
• Data mining is an issue for
user modeling which causes
ethical issues in terms of
races, gender and sexual
orientations.
• HRS require less expertise to
operate because of its autonomic
operability and collaborative-
filtering approaches
• Integrating records of personal
health data can enhance
predictive power of HRS and
may solve the cold start problem
• Collaborative approach may
enable gathering user preferences
and knowledge in HRS, which
are not very common but can be
useful in health education
• Autonomic structure can enhance
recommendations in terms of
consistency, and it improve
knowledge gathered from the
users
on semantics in development phase of HRS. In addition to
that, Software-oriented Architecture (SOA) was commonly
the basis for development approach [1, 8]. Its modularity
and compatibility with web services made SOA favorable in
HRS development. However, the main element of HRS lies
behind the algorithms. It is crucial to develop the algorithm
with high prediction rates in terms of user behavior [11].
From methodological side, it was observed that content
based and collaborative based filtering were commonly
used, however, hybrid RS and emerging methods in
filtering
(profile-based) were also being applied in increasing
manner.
It was observed that filtering methods, SOA approaches
and linguistic studies were combined in a platform of HRS
to create algorithms. Thus, significance of algorithms is at
the paramount in terms of health affairs. Considering the
privacy concerns in RS as well as the delicacy of human
health records, HRS algorithms were restricted by strict
limitations in this context.
On the other side, the challenges and opportunities of
HRS were discussed in several studies [1, 2, 12]. The main
challenge was outlined regarding to privacy, and the
opportunity was its contribution in diagnostic process.
Table 1 presented the challenges and opportunities
underlined in the studies.
IV. CONCLUSION
In this study, a literature review on Health Recommender
Systems was conducted, and the findings were presented.
The main conclusion is that HRS are a promising
development for healthcare services. The studies
demonstrated that HRS have been branched out in different
fields of health industry, and HRS applications have been
increasingly embedded in the health service systems.
Considering the literature, there were number of studies
related to HRS use, design and methodologies. In this
respect, it was found that emerging HRS studies were also
in a rising trend in the literature. However, HRS domain is
relatively new, thus it needs time to present mature
researches and to improve filtering algorithms. In addition
to that, privacy issues constitute a major concern to
overcome.
In this paper, it was aimed to contribute literature by (1)
giving an opinion about the literature of HRS, (2)
underlining the studies about HRS, and (3) providing a set
of review methods for further studies in the field. It
constituted a preliminary study, thus, further studies
covering broader set of criteria, as well as academic
journals, can be conducted as the next step in literature
review of HRS.
REFERENCES
[1] L. Fernandez-luque, R. Karlsen and L.K. Vognild, "Challenges and
Opportunities of using Recommender Systems for Personalized Health
Education” Stud. Health Technol. Inform., 150(903), pp. 903-7, 2009.
[2] F. Ricci, L. Rokach, B. Shapira and P. B. Kantor, Introduction to
Recommender Systems Handbook, pp. 257-297, Springer, Berlin,
2011.
[3] D. H. park, H. K. Kim, I. Y. Choi and J. K. Kim, “A literature review
and classification of recommender systems research”, J. Expert Syst.
Appl., 39 (11), pp.10059-10072, 2012.
[4] J. Kim, J. Lee, J. Park and Y. Lee, “Design of Diet Recommendation
System for Healthcare Service Based on User Information”, Fourth
International Conference on Computer Sciences and Convergence
Information Technology, ICCIT '09, pp.516-518, 24-26 Nov 2009.
[5] P. Pattaraintakorn, G. Zaverucha and N. Cercone, “Web Based Health
Recommender System Using Rough Sets, Survival Analysis and Rule-
Based Expert Systems”, Proceedings of the 11th International
Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular
Computing, pp. 491-499,13-16 May 2007.
[6] A. Sami, P. Nagatomi, M. Terabe, and K. Hashimoto, “Design of
Physical Activity Recommendation System”, Proceedings of IADIS
International Conference on e-Learning, pp. 148-152, 22-25 July 2008.
[7] M. Wiesner, “Adapting recommender systems to the requirements of
personal health record systems”, Proceedings of ACM International
Health Informatics Syposium, pp. 410-414, 11-12 November 2010.
[8] C. Lee, M. Lee, and D.A. Han, “Framework for Personalized
Healthcare Service Recommendation. Health”, Proceedings of 10th
International Conference on e-health Networking, Applications and
Services, pp. 90-95, 7-9 July 2008.
[9] N. Ramakrishnan, B. J. Keller, B. J. Mirza, A. Y. Grama and G.
Karypis, “When being Weak is Brave: Privacy Issues in Recommender
Systems”, IEEE Internet Comput., 5(6), pp. 54 - 62, 2001.
[10] B. Kitchenham, “Procedures for Performing Systematic Reviews”,
Technical Report TR/SE-0401, Keele University, NICTA, 2004.
[11] M. Lopez-Nores, Y. Blanco-Fernandez, J.J. Pazos-Arias, J. Garcia-
Duque and M.I. Martin-Vicente, “Enhancing Recommender Systems
with Access to Electronic Health Records and Groups of Interest in
Social Networks”, Proceedings of 7th International Conference on
Signal-Image Technology and Internet-Based Systems, pp. 105-110, 28-
30 Nov 2011.
[12] S. Mika, “Challenges for Nutrition Recommender Systems”. American
Journal of Public Health, Proceedings of the 2nd Workshop on Context
Aware Intelligent Assistance, pp.25-33, 4 October 2011.
[13] T.G. Morrell and L. Kerschberg, “Personal Health Explorer: A
Semantic Health Recommendation System”, Proceedings of the 28th
International Conference on Data Engineering Workshops, pp. 55-
59,1-5 April 2012.
[14] G. Adomavicius, and A. Tuzhilin, “Toward the next generation of
recommender systems: A survey of the state-of-the-art and possible
extensions”, IEEE T. Knowl. Data En., 17(6), pp. 734-749, 2005.

4. APPENDIX A. FILTERING TYPES, DEFINITIONS, PROS & CONS AND EXAMPLES [1, 2, 14]
Type Definition Pros Cons Example systems
Collaborative
filtering
Gathering and analyzing information
about activities and behaviors, of users
and predicting what users is likely to do
regarding to their similarity with other
users
It is able to recommend
complex items
Sparsity, Cold start and
scalability issues
Last.FmTM
, amazonTM
,
facebookTM
,
myspaceTM
, linkedinTM
Content-based
filtering
Examining the historical data and current
preferences of users and predicting based
on characteristics of the items
Kick-off information is not
required to be much
Scope of recommendation
source is limited
Internet Movie
DatabaseTM
(IMDB.com)
Hybrid
recommender
systems
Combining content-based capabilities
with collaborative-based approach or
unifying the approaches into one unique
model
Highly accurate and effective
results in recommendations
than other approaches.
Solution to cold start and
sparsity issues
The need of more
knowledge engineering
NetflixTM
Knowledge-based
recommender
systems
Gathering knowledge about users and
generating approach to provide a
recommendation by reasoning about what
products can meet user needs
No ramp-up problem because
recommendations do not
depend on user ratings
Suggestion ability is static The restaurant
recommender entree
Mobile
Recommender
Systems
In addition to the traditional approaches,
mobile RS involve geographic data and
enable context sensitive recommendations
Effective results on regional
based recommendations
Heterogeneous and noisy
environment;
transplantation, validation
and generality problems.
Taxi Routing apps
APPENDIX B. FINDINGS OF LITERATURE REVIEW
Paper Aim of the paper Methods Ref #
A Framework for Personalized
Healthcare Service
Recommendation
A personalized healthcare service
recommendation framework that considers
consumers’ health status to find adequate
services for them.
Content based RS; SOA, web based contents
(HTML,XML, web portal); DCAP algorithm to
get measurable standard health status data
3
Web Based Health Recommender
System Using Rough Sets,
Survival Analysis And Rule-
based Expert Systems
Providing accurate, low-cost clinical examination
recommendations given patients’ self- reported
data
Content base RS; Rough sets, survival analysis
(reliability) and rule-based expert systems
(recommendation rules like AND,OR); Forward
chaining:(this approach begins with a set of facts
and rules, and tries to find a way of using those
rules and facts to deduce a recommendation or a
suitable action)
6
Adapting Recommender Systems
To The Requirements Of
Personal Health Record Systems
To supply Personal Health Records system users
relevant, individually tailored health information
by developing HRS with emphasis on semantics
Profile based RS 8
Enhancing Recommender
Systems with Access to
Electronic Health Records and
Groups of Interest in Social
Networks
A semantics-based recommender system devised
to embed selected advertisements in Digital TV
programs - deal with the risks that arise from
ignorance or commercial interest in social
context
Text mining and association mining; Hybrid
recommendation system; Semantic-web rule
language
4
Design Of Physical Activity
Recommendation
Leisure time physical activity RS by collecting
checkup data of people and recommend basing
on similar people exercise
Collaborative based RS; Building ontology for
physical activities- distance; Categories by effort
level
7
Design of Diet Recommendation
System for Healthcare Service
Based on User Information
Providing a personalization diet recommended
service for the users who require the prevention
and management for coronary heart disease
Agent networks (vital signs data recording) 2
Challenges for Nutrition
Recommender Systems
difficulties and challenges in nutrition
recommender systems which make suitable
suggestion based on user profile
No system provided. Development was
suggested on user ratings and nutritional needs
5
Challenges and Opportunities of
Using Recommender Systems for
Personalized Health Education
Exploring the usage of RS for Health Education Hybrid based; SOA; CTHES (the algorithms for
personalization are based on the human expert
knowledge)
1