cross-context multi-level interpretations to generate personalized recommendations
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8/17/2019 Cross-Context Multi-level Interpretations to Generate Personalized Recommendations
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Cross-Context Multi-level Interpretations to generate
Personalized RecommendationsSyed Imran Ali and Sungyoung Lee
Department of Computer Science and Engineering Kyung Hee University
Seocheon-dong !iheung-gu "ongin-si !yeonggi-do #epu$lic of Korea %%&-'()
* imran+ali sylee ,osla$+.hu+ac+.rA$stract
A huge array of personalized healthcare and wellness systems are introduced for digital health and
Quantified Self movement in recent years. These systems share common features which include
tasks such as self-tracking, self-quantifications, and self-monitoring ased on the raw sensory data.These features assist users to e more aware of their health. !owever, such systems cannot e
considered sufficient enough for changing the unhealthy haits of users. To induce healthy haits in
users, system needs to provide situation aware personalized recommendations. "ne ig challenge
in this regard is the careful interpretations of user conte#t, environmental variales, and user
preferences. $e propose an innovative method for cross-conte#t multi-level interpretations to
generate personalized health and wellness recommendations. $ith such interpretations, the self-
quantification systems can e advanced to generate personalized recommendations in addition to
tracking, quantifying, and monitoring. %y achieving this, we can increase the ownership of the
application y the user, thus increase the chances of healthy haits promotion and induction.
1. IntroductionA huge array of systems for personalized healthcare and
wellness management has rapidly grown during the recentyears due to the rapid increase in wearale and moile
technologies &'(. These systems and applications have
augmented the portfolio of digital health and
quantification-self movement. $ith advances in Self-
Quantification, systems capturing and recording data of
human health and fitness are now feasile. $ith the
availaility of this data, the systems provide etter
understanding of users) health status and their
relationship to the world around them &*(. Self-
quantification systems and applications are of high
importance to educate users to e more aware of their
health status, ut alone this cannot e consideredsufficient to change unhealthy haits in users. +hanging
unhealthy haits of users, demands to provide actionale
recommendations. #isting systems such as itit le# &(,
/awone 0p &1(, and 2isfit Shine &3( are some e#amples
of providing some asic recommendations ased on the
measured steps and slept hours. Samsung S !ealth &4(
and 5oogle it &6( are working as personal fitness coach
and health-tracking platform respectively on the asis of
capturing user steps counting.
7reviously, we proposed an innovative digital health
framework called 2ining 2ind &', 8( for personalized
healthcare and wellness support. 9t provides personalized
recommendations on the asis of e#pert knowledge and
domain guidelines. The main limitation with e#isting
recommendation is that they are ased on user profile
information without taking into account user)s current
conte#t, preferences, and environmental variales. To get
personalized recommendation services, a granular level of user)s preference information is also required along with
conte#tual information and environmental variales. 9n this
paper, we proposed an innovative method to interpret the
user situation on three parameters: user conte#t ;low level
and high level<, physical conte#t ;weather<, and user
preferences.
. Met!odAs shown in ig. ', the high level architecture of the
proposed method depicts the overall functional workflow.
At first level, the data is collected from wearale sensors
and smart phone. The data is curated and persisted with alogical division of user profile data, conte#tual data, and
environmental data. The user profile data includes user
demographics ;gender, age< and physiological factors
;height and weight<. +onte#tual data is categorized into
two type of conte#ts: low level and high level conte#ts. =ow
level conte#t includes location, emotion, and physical
activities. !igh level conte#t includes the conte#t
determined from the low level conte#t, such as office-
works, commuting, eating, and others. nvironmental
variales include weather conte#t as rainy, sunny, etc.
>nowledge %uilder component reasons over user profile
data and physical activity data along with rules of
knowledge ase to initial level of recommendations. or an
instance, let the system generate recommendation as? @an
hour e#ercise is required to urn # amount of calories.
8/17/2019 Cross-Context Multi-level Interpretations to Generate Personalized Recommendations
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This is a generic level recommendation statement that can
e interpreted differently, such as? walking m' minutes or
running m* minutes or hiking m1 minutes or others. rom
the guidelines, a set of possile recommendations can e
inferred for an hour e#ercise to urn # amount of calories.
As per the guidelines, all the aove recommendations are
correct, however, a suset of them is applied to the user
according to hisBher current situation. Also, it is possile
that the recommendation uilder generates one
recommendation, e.g. @hiking # minutes, ut hiking
recommendation may not e permitted ecause of the
weather condition is rainy. 5enerate the personalized
recommendations we need to interpret the
recommendation on the asis of user situations.
Cecommendation 9nterpreter in ig. ', gets the initial
recommendation uilt y recommendation uilder
component and interpret with the strategy discussed in
following section.
2.1. Design Strategy of Multi-level Interpretations of recommendations And Results$e devised a matri# representation for each conte#t and
environmental variale to map against each type of
recommendations such as running, walking, hiking,
Dogging, and others. An e#ample of one of the low level
conte#ts location is shown in Tale '.
Figure 1: High level architecture of proposed approach of
generating personalized recommendations on the basis of userprofile, context, and environmental data.
Similar matrices are created for all possile conte#ts and
environmental variales. "n the asis of current instance
of user conte#t and environmental variale;s<, we generate
a conte#tual matri#. 9n the conte#tual matri#, the current
instance of conte#t B environmental variale is crossed
with generated recommendation. Suppose current
instance has the values as loc E home, high level conte#t
;!+=< E amusement, emotion E happy, and weather E
rainy and the recommendation generated y recommendation uilder is hiking. 7rior to interpretations,
we first look at the column of recommended activity and
draw the interpretations on the asis of AFG operation. or
instance, one of the possile interpretations is? the
recommendation in the current situation is not applicale
and we need to find alternative recommendations. $e
apply the methods y selecting 3 potential users and
validate the results for different situations.
Table 1: Location context values mapping with differentrecommendations 1 shows applicable, ! shows not applicable"
Loc#$ec Hi%ing &ogging 'itting 'tretching ((
)*m ! 1 1 1 ((
Home ! 1 1 1 ((
+ffice ! ! 1 1 ((
((( (( (( (( (( ((
Tale * shows an e#ample of conte#tual matri# and
aggregate vector creation. +onte#tual matri# is composed
of current conte#ts of the user. All the current conte#ts
e.g. locations, !=+, weather, emotion, etc. are evaluatedin terms of recommended activities. =ogical AFG-operation
is performed on each of the physical activity ;column-wise
manner< to generate aggregate vector. This aggregate
vector contains a list of physical activities which can e
performed in the given conte#t of the user.Table : 'ample contextual matrix and aggregate vector 1 showsapplicable, ! shows not applicable"
-ontext#$ec
Hi%ing
&ogging 'itting 'tretching ((
Loc Home ! 1 1 1 (
HL-/nactivit*
1 1 ! 1 (
0eather$ain*
! 1 1 1 (
((( (( (( (( (( (
Aggregate
Vector
! 1 ! 1
". Conclusion and #uture $or% The coming era in health and wellness services is all
aout inducing healthy haits in users. $e presented our
work on situation-aware recommendation interpretation onthe asis of user situation including conte#t,
environmental variale, and user preferences. $e foresee
enormous application of the proposed approach in health
and wellness applications. The current systems and
applications can enhance their capailities to employ our
interpretation service.
Acknowledgement. This research was supported y the
2S97;2inistry of Science, 9+THuture 7lanning<, >orea,
under the 9TC+;9nformation Technology Cesearch +enter<
support program supervised y the F97A;Fational 9T
9ndustry 7romotion Agency<I ;F97A-*J'1-;!JJ'-'1-'JJ<
Re&erences12.3anos +, 3ilal(4min 5, 4li(6han 0, 4fzel 5, 4li T, 6ang
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3H, Lee '. 5ining 5inds: 4n innovative framewor% forpersonalized health and wellness support. /n:7roceedings of the /nternational -onference on7ervasive -omputing Technologies for Healthcare,/stanbul, Tur%e*8 !19".
2.4lmal%i 5, 5artin('anchez F, )ra* 6. 'elf(uantification: the informatics of personal datamanagement for health and fitness. !1;".
;2.<Fitbit Flex,= http:##www.fitbit.com#flex, accessed:!19(1!(9. !19".
>2.<&awbone ?p,= https:##@awbone.com#up, accessed:!19(1!(A. !19".
92.<5isfit 'hine,=
http:##www.misfitwearables.com#products#shine,accessed: !19(1!(B. !19".
B2.C' Health, C http:##shealth.samsung.com#, accessed:!19(11(!1. !19".
D2.C)oogle Fit, C https:##fit.google.com#, accessed: !19(
11(!. !19".A2. 3anos +, 4min 53, 6han 04, 4fzel 5, 4hmad 5, 4li 5,
4li T, 4li $, 3ilal 5, Han 5. 4n /nnovative 7latform for7erson(-entric Health and 0ellness 'upport. /n:3ioinformatics and 3iomedical Engineering: 'pringer8p.1;1(1>!, !19".
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