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in
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larly
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e
d
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ize
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m
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t
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les
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se
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re
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n
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l
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g
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n
m
o
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u
le
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n
d
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re
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o
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m
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n
d
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ti
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e
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h
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e
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le
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s
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m
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d
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m
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t
th
e
m
t
o
se
lec
t
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o
m
v
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ri
o
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s
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io
n
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se
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e
n
tl
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th
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m
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eled
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m
e
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ts
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ch
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n
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ts
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s
e
r
p
r
e
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ce
s
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is
to
r
ical
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eh
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v
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r
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a
n
d
r
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l
-
tim
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o
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m
atio
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,
t
o
p
r
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v
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tailo
r
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d
s
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g
g
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s
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s
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cc
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m
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d
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k
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r
’
s
g
u
id
ed
n
av
ig
atio
n
to
i
n
ter
esti
n
g
o
r
h
elp
f
u
l
item
s
with
in
a
s
ig
n
if
ican
t
d
ata
s
p
ac
e
[
3
]
,
in
cl
u
d
in
g
tr
a
v
el
[
4
]
,
clo
u
d
s
er
v
ices
[
5
]
,
in
te
r
est
o
f
th
in
g
s
(
I
o
T
)
[
6
]
,
b
o
o
k
s
[
7
]
,
an
d
e
-
lear
n
i
n
g
[
8
]
.
Ma
n
y
r
ec
o
m
m
en
d
atio
n
s
tr
ate
g
ies
h
av
e
b
ee
n
p
u
t
f
o
r
th
in
an
ef
f
o
r
t
to
ac
co
m
p
lis
h
th
is
[
9
]
.
T
h
e
aim
o
f
th
i
s
p
ap
er
is
to
p
r
esen
t
an
o
v
er
v
iew
o
f
p
o
p
u
la
r
r
ec
o
m
m
en
d
atio
n
tec
h
n
iq
u
es.
I
n
p
a
r
ticu
lar
,
it
e
x
am
in
es
th
r
ee
wid
ely
r
ec
o
g
n
ized
m
eth
o
d
s
:
co
n
ten
t
-
b
ased
,
co
lla
b
o
r
ativ
e
f
ilter
in
g
-
b
ased
,
an
d
h
y
b
r
id
ap
p
r
o
ac
h
es
[
1
]
,
[
2
]
.
L
astl
y
,
it
p
r
o
v
id
es
s
o
m
e
k
ey
ch
allen
g
es
with
s
tr
o
n
g
ju
s
tific
atio
n
s
f
o
r
t
h
is
em
er
g
in
g
a
n
d
g
r
o
win
g
f
ield
,
s
p
ec
if
ically
in
to
u
r
is
m
.
T
r
av
el
is
a
v
ast
s
ea
o
f
in
f
o
r
m
at
io
n
,
an
d
d
e
v
elo
p
in
g
an
ef
f
ec
tiv
e
to
u
r
is
m
s
tr
ateg
y
d
em
an
d
s
tim
e,
en
er
g
y
,
an
d
e
x
p
er
tis
e.
T
h
e
p
e
r
s
o
n
al
to
u
r
is
m
RS
f
o
r
Sau
d
i
Ar
a
b
ia
’
s
Qass
im
r
eg
io
n
aim
s
to
s
tr
ea
m
lin
e
th
is
p
r
o
ce
s
s
b
y
s
u
g
g
esti
n
g
o
p
tim
al
tr
a
v
el
itin
e
r
ar
ies.
T
h
is
s
y
s
tem
a
d
ju
s
ts
to
t
h
e
in
ter
ests
an
d
p
r
ef
er
e
n
ce
s
o
f
to
u
r
is
ts
an
d
v
is
ito
r
s
,
ef
f
icien
tly
g
ath
e
r
in
g
,
a
n
aly
zin
g
,
an
d
p
r
esen
tin
g
in
f
o
r
m
atio
n
to
cr
af
t p
er
s
o
n
alize
d
itin
er
ar
ie
s
.
T
h
is
s
tu
d
y
in
v
esti
g
ates
th
e
ef
f
ec
ts
o
f
p
er
s
o
n
alize
d
RS
o
n
en
h
an
cin
g
to
u
r
is
t
ex
p
er
ien
ce
s
in
th
e
Qass
im
r
eg
io
n
o
f
Sau
d
i
Ar
a
b
ia.
W
h
ile
p
r
ev
io
u
s
s
tu
d
ies
h
a
v
e
ex
p
l
o
r
e
d
th
e
im
p
ac
t
o
f
RS
in
to
u
r
is
m
,
th
ey
h
av
e
p
r
im
ar
ily
f
o
cu
s
ed
o
n
g
en
e
r
al
m
o
d
els
with
o
u
t
ad
d
r
ess
in
g
th
e
s
p
ec
if
ic
i
n
f
lu
en
ce
o
f
lo
ca
l
cu
ltu
r
al
an
d
g
eo
g
r
a
p
h
ic
f
ac
to
r
s
o
n
to
u
r
is
ts
’
p
r
ef
er
en
ce
s
an
d
e
x
p
er
ien
ce
s
.
Ad
d
itio
n
ally
,
ea
r
li
er
r
esear
ch
h
as n
o
t
ex
p
licitly
ex
am
in
ed
h
o
w
th
ese
s
y
s
tem
s
ca
n
d
y
n
am
ically
ad
ju
s
t
to
ch
an
g
in
g
u
s
er
p
r
ef
er
en
c
es
with
in
a
s
p
ec
if
ic
r
eg
io
n
.
T
h
is
s
tu
d
y
f
ills
th
es
e
g
ap
s
b
y
tailo
r
in
g
th
e
RS
to
th
e
u
n
iq
u
e
ch
ar
ac
ter
is
tics
o
f
th
e
Qass
im
r
eg
io
n
,
aim
in
g
to
p
r
o
v
id
e
a
m
o
r
e
ta
r
g
eted
an
d
cu
ltu
r
ally
r
elev
an
t t
r
av
el
e
x
p
er
ien
ce
.
T
h
is
s
tu
d
y
i
n
tr
o
d
u
ce
s
a
p
er
s
o
n
alize
d
RS
s
p
ec
if
ically
d
esig
n
ed
f
o
r
th
e
Qass
im
r
eg
io
n
.
I
ts
m
ai
n
o
b
jectiv
e
is
to
p
r
o
v
id
e
tailo
r
e
d
r
ec
o
m
m
en
d
atio
n
s
an
d
s
u
g
g
e
s
tio
n
s
b
ased
o
n
u
s
er
p
r
e
f
er
en
c
es
an
d
s
to
r
ed
d
ata.
T
h
e
to
u
r
is
m
RS
u
tili
ze
s
f
il
ter
in
g
tech
n
iq
u
es
to
id
en
tif
y
a
n
d
r
ec
o
m
m
en
d
d
esti
n
atio
n
s
t
h
at
p
r
o
m
is
e
a
n
i
d
ea
l
ex
p
er
ien
ce
.
A
k
ey
a
d
v
an
tag
e
is
th
at
tr
av
eler
s
ca
n
cu
s
to
m
iz
e
th
eir
itin
er
ar
ies,
s
ig
n
if
ican
tl
y
r
ed
u
ci
n
g
th
e
tim
e
an
d
ef
f
o
r
t r
e
q
u
ir
ed
t
o
f
in
d
lo
c
atio
n
s
th
at
m
atch
th
eir
in
te
r
ests
.
−
Mo
tiv
atio
n
a.
Pro
p
o
s
in
g
a
p
er
s
o
n
alize
d
R
S
f
o
r
Qass
im
r
eg
io
n
o
f
th
e
Kin
g
d
o
m
o
f
Sau
d
i
Ar
ab
ia:
th
e
p
r
o
p
o
s
ed
s
y
s
tem
wi
l
l
h
elp
to
p
r
o
v
id
e
p
e
r
tin
en
t
r
ec
o
m
m
en
d
atio
n
.
I
t
also
alter
s
v
is
ito
r
s
’
an
d
to
u
r
is
ts
’
in
ter
ests
an
d
in
clin
atio
n
s
.
T
o
h
elp
th
e
u
s
er
d
esig
n
a
s
u
itab
le
i
tin
er
ar
y
,
th
e
p
er
s
o
n
al
to
u
r
is
m
RS
co
llects,
ass
es
s
es,
an
d
d
is
p
l
ay
s
in
f
o
r
m
atio
n
in
th
e
m
o
s
t e
f
f
icien
t w
ay
p
o
s
s
ib
le.
b.
Sav
in
g
tim
e
an
d
ef
f
o
r
t:
th
e
p
r
o
p
o
s
ed
s
y
s
tem
is
d
esig
n
ed
to
o
f
f
er
r
ec
o
m
m
en
d
at
io
n
s
an
d
s
u
g
g
esti
o
n
s
b
ased
o
n
u
s
er
p
r
ef
er
en
ce
s
an
d
co
llec
ted
d
ata
.
T
h
e
s
y
s
tem
s
u
g
g
ests
tr
av
el
d
esti
n
atio
n
s
th
at
o
f
f
er
t
h
e
b
est
p
o
s
s
ib
le
ex
p
er
ien
ce
,
allo
win
g
to
u
r
is
ts
to
s
av
e
tim
e
a
n
d
ef
f
o
r
t
b
y
cu
s
to
m
izin
g
th
ei
r
s
ch
ed
u
les
to
m
atch
t
h
eir
p
r
ef
er
en
ce
s
a
n
d
av
o
i
d
in
g
lo
ca
t
io
n
s
th
at
d
o
n
’
t a
lig
n
with
th
eir
in
ter
ests
.
−
Hig
h
lig
h
ts
T
h
e
m
ain
o
b
jectiv
es o
f
th
e
p
r
e
s
en
t w
o
r
k
in
clu
d
e:
a.
I
n
tr
o
d
u
cin
g
a
n
o
v
e
r
v
iew
o
f
t
h
e
co
n
ce
p
ts
o
f
RS
,
th
e
to
u
r
is
m
R
S
an
d
th
e
m
o
s
t
u
s
ed
liter
atu
r
e
tech
n
iq
u
es
o
f
RS
an
d
d
is
cu
s
s
ed
th
eir
s
tr
en
g
th
s
an
d
wea
k
n
ess
es.
b.
R
ev
iewin
g
th
e
cu
r
r
en
t
r
ec
o
m
m
en
d
atio
n
-
b
ased
ap
p
r
o
ac
h
e
s
f
o
cu
s
ed
o
n
to
u
r
is
m
f
ield
.
W
e
d
is
cu
s
s
th
e
s
tr
en
g
th
s
an
d
th
e
wea
k
n
ess
es
o
f
th
ese
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
es
to
id
en
tif
y
th
e
ap
p
r
o
p
r
iate
tec
h
n
o
lo
g
y
f
o
r
o
u
r
p
r
o
p
o
s
al
s
y
s
tem
.
c.
Pro
p
o
s
in
g
a
p
er
s
o
n
alize
d
to
u
r
i
s
m
R
S
fo
r
Qass
im
r
eg
io
n
o
f
th
e
Kin
g
d
o
m
o
f
Sau
d
i
Ar
ab
ia
u
s
i
n
g
co
llab
o
r
ativ
e
f
ilter
in
g
as a
r
ec
o
m
m
en
d
atio
n
tech
n
iq
u
e.
d.
Ass
es
s
in
g
th
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
s
u
g
g
ested
R
S
by
u
tili
zin
g
a
r
ea
l
d
ataset
g
ath
e
r
ed
f
r
o
m
v
ar
io
u
s
s
o
u
r
ce
s
,
in
clu
d
in
g
G
o
o
g
le
Ma
p
s
.
T
h
e
d
ataset
co
n
tain
s
Qass
im
r
eg
io
n
d
ata
r
e
g
ar
d
in
g
u
s
er
i
n
f
o
r
m
a
tio
n
,
attr
ac
tio
n
s
,
ch
alets,
h
er
itag
e
lan
d
m
a
r
k
s
,
p
ar
k
s
,
r
estau
r
an
ts
,
p
u
b
lic
p
lace
s
,
m
alls
,
an
d
h
o
tels
.
T
h
e
s
tr
u
ctu
r
e
o
f
th
is
p
ap
er
is
o
u
tlin
ed
as
f
o
llo
ws:
s
ec
tio
n
2
p
r
o
v
i
d
es
an
o
v
e
r
v
iew
o
f
RS
.
Sectio
n
3
s
u
r
v
ey
s
th
e
c
u
r
r
en
t
liter
atu
r
e
o
n
th
e
s
u
b
ject.
Sectio
n
4
d
etails
th
e
r
esear
ch
m
et
h
o
d
o
lo
g
y
em
p
lo
y
ed
in
th
is
s
tu
d
y
.
Sectio
n
5
ad
d
r
ess
es
th
e
s
y
s
tem
’
s
ev
alu
atio
n
,
en
c
o
m
p
as
s
in
g
d
ata
co
llectio
n
,
an
aly
s
is
,
an
d
in
t
er
p
r
etatio
n
.
L
astl
y
,
s
ec
tio
n
6
s
u
m
m
ar
izes
th
e
p
ap
er
an
d
d
is
cu
s
s
es
p
o
ten
tial
f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
Fig
u
r
e
1
illu
s
tr
ates
th
e
p
ap
er
’
s
r
o
a
d
m
ap
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
2
,
May
20
25
:
9
6
0
-
9
7
4
962
Fig
u
r
e
1
.
Pap
er
r
o
a
d
m
ap
2.
RE
CO
M
M
E
NDA
T
I
O
N
S
YS
T
E
M
:
B
ACK
G
RO
UN
D
T
h
is
s
ec
tio
n
b
r
ief
ly
in
tr
o
d
u
c
es
th
e
co
n
ce
p
ts
o
f
RS
,
th
e
t
o
u
r
is
m
RS
an
d
th
e
m
o
s
t
u
s
ed
liter
atu
r
e
tech
n
iq
u
es o
f
RS
.
2
.
1
.
Rec
o
mm
enda
t
io
n
s
y
s
t
em
RS
ar
e
to
o
ls
th
at
in
ter
ac
t w
ith
lar
g
e
in
f
o
r
m
atio
n
s
p
ac
es.
T
h
e
y
aim
to
r
ed
u
ce
th
e
s
ea
r
c
h
ef
f
o
r
t o
f
u
s
er
s
b
y
p
r
o
v
id
in
g
t
h
em
m
ea
n
in
g
f
u
l
r
ec
o
m
m
en
d
atio
n
s
o
r
b
y
d
i
r
e
ctin
g
th
em
to
u
s
ef
u
l
r
eso
u
r
c
es
with
in
a
lar
g
e
d
ata
s
p
ac
e
[
2
]
,
[
3
]
.
R
S
lev
er
ag
e
t
h
e
o
p
in
io
n
s
o
f
c
o
m
m
u
n
ity
m
em
b
er
s
to
h
elp
in
d
iv
id
u
als
f
in
d
in
f
o
r
m
atio
n
o
r
p
r
o
d
u
cts
th
at
ar
e
m
o
s
t
lik
ely
to
b
e
o
f
in
t
er
est
o
r
r
ele
v
an
ce
t
o
th
em
.
T
o
d
ay
,
R
S
is
im
p
lem
en
te
d
o
n
n
e
ar
ly
all
e
-
c
o
m
m
er
ce
web
s
ites
,
aid
in
g
a
v
ast
n
u
m
b
e
r
o
f
u
s
er
s
.
T
h
ese
s
y
s
tem
s
ty
p
ically
u
tili
ze
co
llab
o
r
ativ
e
f
ilter
in
g
,
c
o
n
ten
t
-
b
ased
f
ilter
in
g
(
also
r
ef
e
r
r
ed
t
o
as
th
e
p
er
s
o
n
ality
-
b
ased
ap
p
r
o
ac
h
)
,
o
r
a
co
m
b
in
atio
n
o
f
b
o
th
,
k
n
o
wn
as
h
y
b
r
i
d
-
b
ased
f
ilter
in
g
,
alo
n
g
with
o
th
e
r
s
y
s
tem
s
lik
e
k
n
o
wled
g
e
-
b
ased
s
y
s
tem
s
.
2.
2
.
T
o
uris
m
re
co
m
m
enda
t
i
o
n sy
s
t
em
A
to
u
r
is
m
RS
s
er
v
es
as
a
p
er
s
o
n
alize
d
to
o
l
f
o
r
to
u
r
is
ts
,
h
elp
i
n
g
th
em
ef
f
icien
tly
s
ea
r
ch
f
o
r
attr
ac
tio
n
s
.
I
t
is
r
eg
ar
d
ed
as
an
ef
f
ec
tiv
e
way
f
o
r
to
u
r
is
ts
to
d
is
co
v
er
p
lace
s
o
f
in
ter
est
[
1
0
]
,
[
1
1
]
.
T
h
e
s
y
s
tem
co
m
p
ar
es
co
llected
d
ata
with
s
im
ilar
an
d
d
if
f
er
e
n
t
d
ata
f
r
o
m
o
th
er
u
s
er
s
,
g
en
e
r
atin
g
a
lis
t
o
f
r
ec
o
m
m
e
n
d
ed
attr
ac
tio
n
s
f
o
r
th
e
to
u
r
is
t.
I
n
th
e
to
u
r
is
m
in
d
u
s
tr
y
,
m
o
d
er
n
tec
h
n
o
lo
g
ies
f
r
o
m
tr
ad
itio
n
al
r
ec
o
m
m
en
d
e
r
s
y
s
tem
s
,
s
u
ch
as
co
llab
o
r
ativ
e
f
ilter
in
g
,
ar
e
co
n
s
id
er
ed
s
u
cc
ess
f
u
l.
First,
th
e
s
y
s
tem
o
f
f
er
s
a
lis
t
o
f
city
attr
ac
ti
o
n
s
lik
ely
to
ap
p
ea
l
to
th
e
u
s
er
,
co
n
s
id
er
i
n
g
th
eir
d
em
o
g
r
a
p
h
ic
p
r
o
f
ile,
p
ast
p
r
ef
er
en
ce
s
,
an
d
c
u
r
r
e
n
t
v
is
it
in
ter
ests
.
Seco
n
d
,
a
p
lan
n
in
g
m
o
d
u
le
o
r
g
a
n
izes
th
e
s
u
g
g
ested
v
en
u
es
b
y
co
n
s
id
er
in
g
th
eir
tim
in
g
a
n
d
th
e
u
s
er
’
s
co
n
s
tr
ain
ts
,
d
eter
m
in
in
g
h
o
w
an
d
wh
en
t
o
en
jo
y
th
e
r
ec
o
m
m
e
n
d
ed
ac
tiv
i
ties
.
A
k
ey
f
ea
tu
r
e
m
is
s
in
g
in
m
o
s
t r
ec
o
m
m
en
d
er
s
y
s
tem
s
i
s
th
e
ab
ilit
y
to
p
r
esen
t
th
e
r
ec
o
m
m
e
n
d
ed
ac
tio
n
s
in
th
e
f
o
r
m
o
f
a
s
tr
u
ctu
r
e
d
ag
e
n
d
a,
o
r
a
n
ac
tio
n
ab
le
p
lan
.
2.
3
.
Rec
o
mm
enda
t
io
n
t
ec
hn
iqu
es
2
.
3
.
1
.
Co
n
t
ent
-
ba
s
ed
f
ilte
ring
s
y
s
t
em
s
C
o
n
ten
t
-
b
ased
f
ilter
in
g
,
also
r
ef
er
r
ed
to
as
co
g
n
itiv
e
f
ilter
in
g
,
is
o
n
e
o
f
th
e
m
o
s
t
p
o
p
u
lar
an
d
ex
ten
s
iv
ely
s
tu
d
ied
r
ec
o
m
m
en
d
atio
n
m
et
h
o
d
s
.
A
cr
u
cial
asp
ec
t o
f
t
h
is
tech
n
iq
u
e
is
th
e
u
s
er
m
o
d
elin
g
p
r
o
ce
s
s
,
wh
er
e
a
u
s
er
’
s
in
ter
ests
ar
e
in
f
er
r
ed
b
ased
o
n
th
e
item
s
th
ey
en
g
ag
e
with
.
T
h
is
in
v
o
l
v
es
co
m
p
ar
in
g
th
e
c
o
n
ten
t
o
f
an
item
with
th
e
u
s
er
’
s
p
r
o
f
ile
[
2
]
.
A
co
n
ten
t
-
b
ased
f
ilter
in
g
s
y
s
tem
u
tili
ze
s
th
e
ter
m
f
r
eq
u
e
n
c
y
(
T
F)
an
d
i
n
v
er
s
e
d
o
cu
m
en
t
f
r
eq
u
e
n
cy
(
I
DF)
m
eth
o
d
[
2
]
,
[
7
]
.
T
h
is
ap
p
r
o
ac
h
g
ath
e
r
s
d
ata
b
y
ca
lcu
la
tin
g
b
o
th
th
e
f
r
eq
u
e
n
cy
o
f
a
te
r
m
an
d
its
in
v
e
r
s
e
d
o
cu
m
e
n
t
f
r
eq
u
e
n
cy
.
T
h
e
g
o
al
is
to
as
s
es
s
th
e
s
ig
n
if
ican
ce
o
f
a
wo
r
d
in
a
tex
t
b
y
co
u
n
tin
g
h
o
w
o
f
ten
it
ap
p
ea
r
s
with
in
a
d
o
cu
m
en
t
a
n
d
d
eter
m
in
in
g
h
o
w
m
a
n
y
d
o
cu
m
en
t
s
c
o
n
tain
th
at
wo
r
d
.
Fre
q
u
en
tly
o
cc
u
r
r
in
g
wo
r
d
s
ar
e
ass
ig
n
ed
a
h
ig
h
er
s
co
r
e.
T
h
e
p
r
o
ce
s
s
ca
n
b
e
s
u
m
m
ar
ized
as
f
o
llo
ws:
i)
T
F
is
th
e
n
u
m
b
e
r
o
f
ti
m
es
a
ter
m
ap
p
ea
r
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
ta
g
-
b
a
s
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r
ec
o
mme
n
d
er sys
tem
fo
r
to
u
r
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m
u
s
in
g
co
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r
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tive
filt
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g
(
A
fef
S
elmi)
963
in
a
d
o
cu
m
en
t d
iv
i
d
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b
y
th
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o
tal
n
u
m
b
er
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f
ter
m
s
,
ii)
I
DF i
s
th
e
lo
g
ar
ith
m
o
f
th
e
to
tal
n
u
m
b
er
o
f
d
o
cu
m
en
ts
d
iv
id
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b
y
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m
b
er
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d
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u
m
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ts
co
n
tain
in
g
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te
r
m
,
a
n
d
iii)
th
e
f
in
al
s
co
r
e
is
th
e
p
r
o
d
u
ct
o
f
T
F(t)
a
n
d
I
DF(
t)
.
2
.
3
.
2
.
Co
lla
bo
ra
t
i
v
e
f
ilte
ring
s
y
s
t
em
s
C
o
llab
o
r
ativ
e
f
ilter
in
g
,
also
r
e
f
er
r
ed
to
as
s
o
cial
f
ilter
in
g
,
g
en
er
ates
r
ec
o
m
m
e
n
d
atio
n
s
b
a
s
ed
o
n
th
e
p
r
ef
er
en
ce
s
o
f
u
s
er
s
with
s
im
ilar
tast
es
[
1
]
.
I
t
m
ak
es
s
u
g
g
e
s
tio
n
s
b
y
a
n
aly
zin
g
a
u
s
er
’
s
t
r
an
s
ac
tio
n
h
is
to
r
y
,
r
atin
g
s
,
o
r
p
u
r
ch
ase
d
ata.
A
d
i
s
tin
ctiv
e
f
ea
tu
r
e
o
f
th
is
tech
n
i
q
u
e
is
th
at
it
d
o
es
n
o
t
tak
e
th
e
item
’
s
attr
ib
u
tes
in
to
ac
co
u
n
t
[
2
]
.
C
o
llab
o
r
ativ
e
f
ilt
er
in
g
s
y
s
tem
s
ty
p
ically
em
p
lo
y
two
m
ain
tech
n
iq
u
es:
−
User
-
b
ased
f
ilter
in
g
:
th
is
m
eth
o
d
ev
alu
ates
h
o
w
clo
s
ely
alig
n
ed
a
u
s
er
’
s
p
r
ef
er
e
n
ce
s
ar
e
with
th
o
s
e
o
f
o
th
e
r
u
s
er
s
.
I
t
id
en
tifie
s
u
s
er
s
with
s
im
ilar
ch
ar
ac
ter
is
tics
to
th
e
tar
g
et
u
s
er
an
d
th
en
r
ec
o
m
m
e
n
d
s
item
s
b
ased
o
n
wh
at
th
o
s
e
s
im
ilar
u
s
er
s
lik
e
an
d
h
o
w
t
h
ey
h
a
v
e
r
ated
v
ar
io
u
s
item
s
.
−
I
tem
-
b
ased
f
ilter
in
g
:
s
im
ilar
ly
to
u
s
er
-
b
ased
f
ilter
in
g
,
t
h
is
m
eth
o
d
m
ea
s
u
r
es
th
e
s
im
ilar
ity
b
et
wee
n
th
e
tar
g
et
item
an
d
o
th
er
item
s
.
I
t
an
aly
ze
s
th
e
u
s
er
-
item
m
atr
ix
,
id
en
tifie
s
item
s
r
ated
b
y
ac
tiv
e
u
s
er
s
,
an
d
th
en
co
m
p
ar
es th
o
s
e
item
s
to
th
e
ta
r
g
et
item
to
g
e
n
er
ate
r
ec
o
m
m
e
n
d
atio
n
s
.
2
.
3
.
3
.
K
no
wledg
e
-
ba
s
ed
s
y
s
t
em
s
Kn
o
wled
g
e
-
b
ased
s
y
s
tem
s
r
ely
en
tire
ly
o
n
d
o
m
ain
-
s
p
ec
if
ic
k
n
o
wled
g
e.
T
h
ey
id
en
tif
y
th
e
u
s
er
’
s
n
ee
d
s
an
d
p
r
ef
er
e
n
ce
s
,
g
e
n
er
atin
g
s
u
itab
le
r
ec
o
m
m
e
n
d
atio
n
s
b
ased
o
n
th
eir
k
n
o
wled
g
e
m
o
d
els.
T
o
ad
d
r
ess
th
e
c
o
ld
-
s
tar
t
p
r
o
b
lem
,
k
n
o
wled
g
e
-
b
a
s
ed
s
y
s
tem
s
ar
e
o
f
ten
s
u
g
g
ested
as
an
alter
n
ativ
e
to
co
n
ten
t
-
b
ase
d
a
n
d
co
llab
o
r
ativ
e
s
y
s
tem
s
.
Ho
wev
er
,
th
is
ap
p
r
o
ac
h
is
co
m
p
u
tati
o
n
ally
in
ten
s
iv
e,
as
it
r
eq
u
ir
es
th
e
ac
q
u
is
itio
n
an
d
r
ep
r
esen
tatio
n
o
f
k
n
o
wled
g
e
f
o
r
all
item
s
in
th
e
r
ec
o
m
m
en
d
er
s
y
s
tem
[
1
2
]
.
2
.
3
.
4
.
Dem
o
g
ra
ph
ic
s
y
s
t
em
s
I
n
d
e
m
o
g
r
a
p
h
ic
f
ilter
in
g
,
u
s
er
s
ar
e
g
r
o
u
p
ed
b
ased
o
n
d
em
o
g
r
ap
h
ic
ch
a
r
ac
ter
is
tics
to
g
en
e
r
ate
r
ec
o
m
m
en
d
atio
n
s
.
Ma
n
y
c
o
m
p
an
ies
u
s
e
th
is
m
eth
o
d
f
o
r
p
r
o
v
id
in
g
r
ec
o
m
m
en
d
atio
n
s
er
v
ices
b
ec
au
s
e
it
is
s
im
p
le,
ea
s
y
to
im
p
lem
e
n
t,
an
d
co
m
p
u
tatio
n
ally
ef
f
icien
t.
T
h
e
d
em
o
g
r
a
p
h
ic
a
p
p
r
o
ac
h
r
eq
u
ir
es
m
ar
k
et
r
esear
ch
to
u
n
c
o
v
er
r
elatio
n
s
h
ip
s
b
etw
ee
n
d
if
f
er
en
t
u
s
er
g
r
o
u
p
s
.
T
h
e
m
o
s
t
c
o
m
m
o
n
way
to
an
aly
z
e
th
ese
co
n
n
ec
tio
n
s
is
b
y
ex
am
in
in
g
u
s
er
p
r
o
f
iles
f
r
o
m
a
co
m
p
an
y
’
s
d
atab
ase
o
r
co
n
d
u
ctin
g
s
u
r
v
e
y
s
am
o
n
g
u
s
er
s
.
Un
lik
e
co
n
ten
t
-
b
ased
o
r
c
o
llab
o
r
ativ
e
s
y
s
tem
s
,
th
i
s
m
eth
o
d
d
o
es
n
o
t
r
ely
o
n
p
r
ev
i
o
u
s
u
s
er
tr
an
s
ac
tio
n
d
a
ta,
wh
ich
is
a
k
ey
ad
v
an
tag
e
[
1
2
]
.
2
.
3
.
5
.
H
y
brid
-
ba
s
ed
f
ilte
ring
s
y
s
t
em
s
A
h
y
b
r
id
f
ilter
in
g
s
tr
ateg
y
i
n
teg
r
ates
m
u
ltip
le
f
ilter
in
g
m
et
h
o
d
s
.
I
t
is
d
esig
n
e
d
t
o
ad
d
r
es
s
co
m
m
o
n
is
s
u
es
a
s
s
o
ciate
d
wi
th
co
n
ten
t
-
b
ased
f
ilter
in
g
,
co
llab
o
r
ativ
e
f
ilter
in
g
,
an
d
k
n
o
wled
g
e
-
b
ased
tech
n
iq
u
es,
s
u
c
h
as
th
e
co
ld
-
s
tar
t
p
r
o
b
lem
,
o
v
e
r
-
s
p
ec
ializatio
n
,
an
d
d
ata
s
p
ar
s
ity
.
Hy
b
r
id
f
ilter
in
g
is
in
tr
o
d
u
ce
d
to
o
v
er
c
o
m
e
th
ese
ch
allen
g
es
an
d
en
h
an
ce
b
o
th
th
e
ac
cu
r
ac
y
an
d
ef
f
icien
cy
o
f
th
e
r
ec
o
m
m
en
d
atio
n
p
r
o
ce
s
s
[
2
]
.
T
h
e
h
y
b
r
i
d
-
b
ased
f
ilter
in
g
s
y
s
tem
in
co
r
p
o
r
ates th
e
f
o
llo
win
g
tech
n
iq
u
es
:
−
W
eig
h
ted
:
th
is
m
eth
o
d
co
m
b
in
es
th
e
s
co
r
es
f
r
o
m
d
if
f
er
en
t
r
ec
o
m
m
en
d
atio
n
co
m
p
o
n
en
ts
s
ta
tis
t
ically
,
ag
g
r
eg
atin
g
th
em
u
s
in
g
an
a
d
d
itiv
e
f
o
r
m
u
la.
−
Switch
in
g
:
th
e
s
y
s
tem
s
elec
t
s
a
s
p
ec
if
ic
r
ec
o
m
m
en
d
atio
n
co
m
p
o
n
e
n
t
f
r
o
m
th
e
av
aila
b
le
o
p
tio
n
s
an
d
ap
p
lies
it.
−
Featu
r
e
c
o
m
b
in
atio
n
:
in
t
h
is
a
p
p
r
o
ac
h
,
two
d
is
tin
ct
c
o
m
p
o
n
e
n
ts
co
n
tr
ib
u
te
to
th
e
r
ec
o
m
m
e
n
d
atio
n
p
r
o
ce
s
s
.
T
h
e
ac
tu
al
r
ec
o
m
m
en
d
e
r
o
p
er
ates
b
ased
o
n
d
ata
m
o
d
if
ied
b
y
th
e
co
n
t
r
ib
u
tin
g
co
m
p
o
n
en
t,
wh
ich
tr
an
s
f
er
s
f
ea
tu
r
es f
r
o
m
o
n
e
s
o
u
r
ce
to
a
n
o
th
er
.
−
Featu
r
e
a
u
g
m
en
tatio
n
:
s
im
ilar
to
f
ea
tu
r
e
co
m
b
i
n
atio
n
,
b
u
t
th
e
co
n
tr
ib
u
tin
g
co
m
p
o
n
en
t
p
r
o
v
i
d
es
n
ew
f
ea
tu
r
es.
T
h
is
m
eth
o
d
is
m
o
r
e
f
lex
ib
le
th
an
f
ea
tu
r
e
c
o
m
b
in
at
io
n
.
−
C
ascad
e:
th
is
tech
n
iq
u
e
s
er
v
es
as
a
tie
-
b
r
ea
k
er
.
E
ac
h
r
ec
o
m
m
en
d
er
is
ass
ig
n
ed
a
p
r
io
r
it
y
,
an
d
lo
wer
-
p
r
i
o
r
ity
r
ec
o
m
m
en
d
er
s
ac
t a
s
tie
-
b
r
ea
k
er
s
f
o
r
h
ig
h
er
-
p
r
io
r
ity
o
n
es.
3.
RE
L
AT
E
D
WO
RK
Sev
er
al
s
tu
d
ies
o
n
RS
in
th
e
to
u
r
is
m
d
o
m
ain
h
av
e
b
ee
n
in
tr
o
d
u
ce
d
in
t
h
e
liter
atu
r
e.
T
h
is
s
ec
tio
n
o
f
f
e
r
s
an
in
-
d
e
p
th
r
e
v
iew
o
f
cu
r
r
en
t
r
esear
ch
an
d
p
r
o
p
o
s
ed
m
eth
o
d
s
.
W
e
an
aly
ze
th
e
s
tr
en
g
th
s
an
d
wea
k
n
ess
es
o
f
th
ese
ap
p
r
o
ac
h
es to
d
eter
m
in
e
th
e
m
o
s
t su
itab
le
tech
n
o
l
o
g
y
f
o
r
o
u
r
p
r
o
p
o
s
al.
I
n
th
eir
s
tu
d
y
L
ee
et
a
l.
[
4
]
,
p
r
o
p
o
s
ed
a
p
er
s
o
n
alize
d
RS
th
at
co
n
s
id
er
s
u
s
er
p
r
ef
er
e
n
ce
s
,
d
iv
er
s
ity
,
an
d
d
is
tan
ce
.
T
h
e
s
y
s
tem
r
ec
o
m
m
en
d
s
to
u
r
is
t
attr
ac
tio
n
s
b
ased
o
n
th
r
ee
s
co
r
es:
p
-
Div
,
p
-
Po
p
,
an
d
p
-
Dis
.
T
o
ev
alu
ate
th
e
ap
p
r
o
ac
h
,
th
e
a
u
th
o
r
s
co
llected
u
s
er
r
atin
g
s
an
d
m
etad
ata
o
f
PO
I
s
f
r
o
m
T
r
ip
Ad
v
is
o
r
an
d
Nav
er
.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ated
t
h
at
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
u
tp
er
f
o
r
m
ed
ex
is
tin
g
o
n
e
s
,
s
h
o
w
i
n
g
i
m
p
r
o
v
e
m
e
n
t
s
i
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
2
,
May
20
25
:
9
6
0
-
9
7
4
964
p
r
e
c
i
s
i
o
n
b
y
2
%
,
2
4
%
,
a
n
d
2
0
%
a
n
d
r
e
c
a
ll
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1
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n
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f
o
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t
o
p
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1
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to
p
-
2
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n
d
to
p
-
3
r
ec
o
m
m
en
d
atio
n
s
,
r
esp
ec
tiv
ely
.
A
d
d
itio
n
ally
,
co
n
s
id
er
in
g
u
s
er
s
’
v
ar
ied
p
r
ef
er
en
ce
s
p
r
o
v
ed
ef
f
ec
tiv
e
in
en
h
an
cin
g
r
ec
o
m
m
en
d
atio
n
s
.
T
h
e
in
ter
n
et
o
f
f
er
s
a
v
ast
a
m
o
u
n
t
o
f
in
f
o
r
m
atio
n
o
n
to
u
r
is
m
an
d
leis
u
r
e
ac
tiv
ities
,
m
ak
in
g
it
ch
allen
g
in
g
f
o
r
tr
a
v
eler
s
to
s
if
t
th
r
o
u
g
h
it
all
to
p
lan
th
eir
t
r
ip
s
.
I
n
th
e
s
tu
d
y
[
1
2
]
,
p
r
o
p
o
s
ed
two
s
o
lu
tio
n
s
:
i)
b
y
ap
p
ly
in
g
b
ig
d
ata
co
n
ce
p
ts
an
d
lev
er
ag
in
g
d
ata
f
r
o
m
o
n
lin
e
co
m
m
u
n
ities
,
co
n
te
x
tu
aliza
tio
n
ca
n
b
e
b
r
o
ad
e
n
ed
to
co
v
er
m
u
ltip
le
d
im
en
s
io
n
s
f
o
r
tr
av
eler
s
,
ad
d
r
ess
in
g
v
ar
io
u
s
asp
ec
ts
o
f
th
eir
jo
u
r
n
ey
;
an
d
ii)
d
ev
elo
p
in
g
n
ew
r
ec
o
m
m
en
d
er
s
y
s
tem
s
as
p
er
s
o
n
aliza
tio
n
an
d
co
n
te
x
tu
aliza
tio
n
co
n
tin
u
e
to
ex
p
an
d
.
C
u
r
r
en
t
s
y
s
tem
s
ca
n
g
en
er
ate
h
ig
h
ly
p
e
r
s
o
n
alize
d
,
r
ea
l
-
tim
e,
an
d
c
o
n
tex
t
-
awa
r
e
r
ec
o
m
m
en
d
atio
n
s
.
Ab
b
asi
-
Mo
u
d
et
a
l
.
[
1
3
]
p
r
o
p
o
s
ed
a
to
u
r
is
m
RS
th
at
co
m
b
in
es
s
em
an
tic
clu
s
ter
in
g
an
d
s
en
tim
en
t
an
aly
s
is
.
T
h
e
s
y
s
tem
u
ti
lize
s
co
n
tex
tu
al
in
f
o
r
m
atio
n
,
in
clu
d
in
g
th
e
u
s
er
’
s
lo
ca
tio
n
(
to
s
u
g
g
est
n
ea
r
b
y
attr
ac
tio
n
s
)
,
tim
e
(
t
o
d
eter
m
in
e
wh
en
attr
ac
tio
n
s
ca
n
b
e
v
is
ited
)
,
a
n
d
wea
th
e
r
(
to
ass
ess
th
e
f
ea
s
ib
ilit
y
o
f
v
is
itin
g
attr
ac
tio
n
s
)
,
to
p
r
o
v
i
d
e
m
o
r
e
s
u
itab
le
r
ec
o
m
m
e
n
d
atio
n
s
.
T
h
e
g
o
al
o
f
t
h
is
ap
p
r
o
ac
h
i
s
to
en
h
an
ce
th
e
r
ec
o
m
m
en
d
atio
n
p
r
o
ce
s
s
b
y
in
co
r
p
o
r
atin
g
all
r
elev
a
n
t
co
n
tex
tu
al
f
ac
to
r
s
.
I
n
ad
d
itio
n
to
im
p
r
o
v
i
n
g
u
s
er
s
atis
f
ac
tio
n
an
d
co
n
v
en
ien
ce
,
s
en
tim
en
t
an
aly
s
is
is
a
ls
o
em
p
lo
y
ed
to
o
f
f
er
a
co
n
te
x
t
-
awa
r
e
RS
.
Ho
wev
er
,
a
k
ey
d
r
awb
ac
k
o
f
th
ese
ap
p
r
o
ac
h
es
,
wh
ich
r
ely
s
o
lely
o
n
lo
ca
tio
n
as
co
n
tex
t
in
f
o
r
m
atio
n
,
is
th
e
ir
o
n
e
-
d
im
en
s
io
n
al
n
atu
r
e
an
d
d
o
m
ain
lim
itatio
n
s
.
Usi
n
g
p
er
s
o
n
alize
d
r
ec
o
m
m
e
n
d
atio
n
tec
h
n
o
l
o
g
y
,
th
e
a
u
th
o
r
s
[
1
4
]
d
e
v
elo
p
ed
a
f
r
am
ewo
r
k
to
b
o
o
s
t
cu
s
to
m
er
lo
y
alty
an
d
en
h
an
c
e
th
e
f
l
o
w
o
f
to
u
r
is
m
p
r
o
d
u
cts
b
etwe
en
o
n
lin
e
an
d
o
f
f
lin
e
p
latf
o
r
m
s
.
T
h
e
f
r
am
ewo
r
k
in
clu
d
es
a
d
esig
n
f
o
r
p
er
s
o
n
alize
d
o
n
lin
e
to
u
r
is
m
r
ec
o
m
m
en
d
atio
n
s
,
c
o
v
er
in
g
t
h
e
p
r
o
ce
s
s
f
r
o
m
d
a
ta
co
llectio
n
to
th
e
s
elec
tio
n
o
f
a
p
er
s
o
n
alize
d
r
ec
o
m
m
e
n
d
atio
n
alg
o
r
ith
m
,
an
d
i
n
teg
r
at
in
g
co
n
ten
t
-
b
ased
r
ec
o
m
m
en
d
atio
n
s
.
A
s
in
g
le
-
r
ec
o
m
m
en
d
atio
n
m
eth
o
d
was
ad
o
p
te
d
as
th
e
p
r
im
ar
y
ap
p
r
o
ac
h
to
in
c
r
ea
s
e
cu
s
to
m
er
en
g
ag
em
en
t
an
d
im
p
r
o
v
e
th
e
e
f
f
icien
cy
o
f
tr
an
s
itio
n
in
g
to
u
r
is
m
p
r
o
d
u
cts
f
r
o
m
o
n
lin
e
to
o
f
f
lin
e.
Ad
d
itio
n
ally
,
t
h
e
f
r
am
ewo
r
k
o
f
f
er
s
a
r
ec
o
m
m
en
d
atio
n
s
tr
at
eg
y
f
o
r
th
e
f
u
tu
r
e
o
p
er
atio
n
a
n
d
m
a
n
ag
em
e
n
t
o
f
o
n
lin
e
to
u
r
is
m
p
r
o
jects.
R
ec
en
tly
,
RS
h
av
e
g
ain
ed
s
ig
n
if
ican
t
atten
tio
n
.
Stu
d
ies
s
h
o
w
th
at
m
an
y
ex
is
tin
g
to
u
r
is
m
RS
p
r
o
v
id
e
m
is
lead
in
g
s
u
g
g
esti
o
n
s
t
h
at
f
ail
to
m
ee
t
to
u
r
is
ts
’
ex
p
ec
tatio
n
s
.
A
k
ey
r
ea
s
o
n
f
o
r
th
is
is
s
u
e
is
th
e
lack
o
f
co
n
s
id
er
atio
n
f
o
r
p
r
ev
io
u
s
u
s
er
r
ev
iews.
T
o
ad
d
r
ess
th
is
,
th
e
s
tu
d
y
[
1
5
]
p
r
o
p
o
s
es
a
s
y
s
tem
th
at
in
co
r
p
o
r
ates
u
s
er
r
ev
iews
in
to
th
e
r
ec
o
m
m
en
d
atio
n
p
r
o
ce
s
s
.
T
h
e
p
r
o
p
o
s
e
d
s
y
s
tem
is
b
ased
o
n
t
h
r
ee
f
ac
to
r
s
:
th
e
n
u
m
b
er
o
f
r
ev
iews,
r
atin
g
s
,
an
d
s
en
tim
en
t.
User
r
ev
iews a
r
e
an
aly
ze
d
an
d
u
s
ed
to
r
ec
o
m
m
en
d
h
o
tels
to
to
u
r
is
ts
,
u
tili
zin
g
a
d
ataset
o
f
E
u
r
o
p
ea
n
h
o
tels
.
T
h
e
s
y
s
tem
’
s
ar
ch
itectu
r
e
in
clu
d
es
f
o
u
r
c
o
m
p
o
n
en
ts
:
in
p
u
t
in
f
o
r
m
atio
n
,
u
s
er
r
ev
iews
(
to
p
ics),
r
ec
o
m
m
e
n
d
a
tio
n
tech
n
iq
u
es,
an
d
o
u
t
p
u
t
in
f
o
r
m
atio
n
.
T
h
e
s
y
s
tem
co
n
s
id
er
s
th
e
lo
ca
tio
n
an
d
u
s
er
r
ev
iews,
a
p
p
ly
in
g
c
o
n
ten
t
-
b
ased
r
ec
o
m
m
en
d
atio
n
s
to
p
r
esen
t
th
e
id
ea
l
h
o
tel
to
t
h
e
u
s
er
in
b
o
th
tex
tu
al
an
d
g
r
ap
h
ical
f
o
r
m
ats.
I
n
th
is
s
tu
d
y
[
1
6
]
,
p
r
esen
ted
th
e
cu
r
r
en
t
p
r
o
g
r
ess
o
f
a
th
e
s
is
p
r
o
ject
f
o
cu
s
ed
o
n
d
ev
elo
p
in
g
n
ew
p
er
s
p
ec
tiv
es
f
o
r
r
ec
o
m
m
en
d
er
s
y
s
tem
s
in
th
e
to
u
r
is
m
d
o
m
a
i
n
,
with
a
p
ar
ticu
lar
f
o
c
u
s
o
n
POI
r
ec
o
m
m
en
d
e
r
s
y
s
tem
s
.
T
h
e
p
r
o
ject
in
c
o
r
p
o
r
ates
ad
d
itio
n
al
co
n
te
x
ts
,
s
u
c
h
as
g
eo
g
r
a
p
h
ical
a
n
d
tem
p
o
r
al
in
f
o
r
m
atio
n
,
to
en
h
an
ce
an
d
ev
alu
ate
th
e
r
ec
o
m
m
en
d
atio
n
s
.
T
h
e
au
th
o
r
s
f
ir
s
t
an
aly
ze
d
th
e
d
if
f
er
e
n
ce
s
b
e
twee
n
POI
RS
an
d
t
r
ad
itio
n
al
RS
,
em
p
h
asizin
g
th
e
im
p
o
r
tan
ce
o
f
co
n
te
x
tu
al
in
f
o
r
m
atio
n
,
esp
ec
ially
g
e
o
g
r
a
p
h
ical
in
f
lu
en
ce
,
an
d
th
e
ch
allen
g
e
o
f
d
ata
s
p
ar
s
ity
.
T
h
e
y
also
d
is
cu
s
s
ed
v
ar
io
u
s
s
tr
ateg
ies
u
s
ed
f
o
r
b
o
th
POI
an
d
s
eq
u
en
tial
r
ec
o
m
m
en
d
atio
n
s
.
Ad
d
itio
n
all
y
,
th
e
au
th
o
r
s
p
r
o
p
o
s
ed
e
x
ten
d
in
g
tr
ad
itio
n
al
in
f
o
r
m
atio
n
r
etr
iev
al
m
etr
ics
to
m
ea
s
u
r
e
r
ec
o
m
m
en
d
atio
n
q
u
ality
,
co
n
s
id
er
in
g
d
im
en
s
io
n
s
b
ey
o
n
d
r
elev
an
ce
,
s
u
ch
as
th
e
tem
p
o
r
al
an
d
g
eo
g
r
a
p
h
ical
c
o
n
t
e
x
t
,
a
n
d
i
n
f
o
r
m
a
t
i
o
n
r
e
l
a
t
e
d
t
o
u
s
e
r
s
a
n
d
i
t
e
m
s
t
o
b
e
t
t
e
r
a
s
s
e
s
s
t
h
e
r
e
c
o
m
m
e
n
d
a
t
i
o
n
s
’
u
s
e
f
u
l
n
e
s
s
f
o
r
t
h
e
u
s
e
r
.
T
h
is
s
tu
d
y
[
1
7
]
i
n
tr
o
d
u
ce
d
a
web
ag
en
t
-
b
ased
in
tellig
en
t
r
e
co
m
m
en
d
atio
n
ap
p
licatio
n
f
o
r
th
e
s
m
ar
t
to
u
r
is
m
s
ec
to
r
,
wh
ich
c
o
m
b
in
es r
ea
l
-
tim
e
d
ata
with
a
h
y
b
r
id
f
ilter
in
g
s
y
s
tem
to
s
u
g
g
est to
u
r
p
ac
k
a
g
es tailo
r
ed
to
clien
t
r
e
q
u
ests
.
T
h
e
au
th
o
r
s
p
r
esen
ted
an
o
n
lin
e
a
u
to
n
o
m
o
u
s
web
ag
en
c
y
d
esig
n
ed
f
o
r
th
e
s
m
ar
t
to
u
r
is
m
ec
o
s
y
s
tem
,
b
u
ilt
o
n
th
e
in
teg
r
atio
n
o
f
v
ar
i
o
u
s
s
ec
to
r
s
with
in
th
e
to
u
r
is
m
in
d
u
s
tr
y
.
T
h
ey
p
r
o
p
o
s
ed
a
n
ef
f
ec
tiv
e
f
ilter
in
g
s
y
s
tem
to
en
ab
le
p
ac
k
ag
e
cu
s
to
m
izatio
n
in
t
h
e
to
u
r
is
m
s
ec
to
r
.
T
h
e
s
tu
d
y
[
1
8
]
ad
d
r
ess
ed
th
e
c
h
allen
g
e
u
s
er
s
f
ac
e
in
q
u
ick
ly
l
o
ca
tin
g
in
f
o
r
m
atio
n
o
f
in
ter
est
.
T
o
tac
k
le
th
is
is
s
u
e,
th
e
au
th
o
r
s
c
o
m
b
in
ed
a
g
en
etic
al
g
o
r
ith
m
(
GA)
w
ith
a
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etwo
r
k
to
p
r
ed
ict
th
e
p
o
p
u
lar
ity
a
n
d
r
atin
g
s
o
f
to
u
r
is
m
s
er
v
ices.
T
h
ey
co
n
s
id
er
ed
th
e
m
u
ltid
im
e
n
s
io
n
al
s
o
cial
in
f
o
r
m
atio
n
o
f
u
s
er
s
with
in
s
o
cial
n
etwo
r
k
s
.
E
x
p
er
im
e
n
tal
r
esu
lts
in
d
icat
ed
th
at
th
is
a
p
p
r
o
ac
h
s
ig
n
i
f
ican
tly
o
u
t
p
er
f
o
r
m
ed
o
th
er
r
ec
e
n
t r
ec
o
m
m
en
d
atio
n
m
eth
o
d
s
in
ter
m
s
o
f
r
ec
o
m
m
e
n
d
atio
n
q
u
ality
.
I
n
th
is
s
tu
d
y
[
1
9
]
,
th
e
a
u
th
o
r
s
aim
ed
to
tack
le
th
e
co
ld
-
s
tar
t
p
r
o
b
lem
i
n
RS
.
T
h
ey
p
r
o
p
o
s
e
d
a
h
y
b
r
id
f
ilter
in
g
ap
p
r
o
ac
h
th
at
co
m
b
i
n
es
co
n
ten
t
-
b
ased
f
ilter
in
g
,
co
llab
o
r
ativ
e
f
ilter
in
g
,
an
d
d
e
m
o
g
r
ap
h
ic
f
ilter
in
g
to
ad
d
r
ess
th
is
is
s
u
e.
T
o
p
r
e
d
ict
r
atin
g
s
f
o
r
n
ew
u
s
er
s
an
d
id
en
ti
f
y
s
im
ilar
item
s
with
in
a
n
eig
h
b
o
r
h
o
o
d
,
th
e
h
y
b
r
i
d
f
ilter
in
g
m
eth
o
d
u
tili
ze
s
d
em
o
g
r
ap
h
ic
in
f
o
r
m
atio
n
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
r
elies
o
n
two
al
g
o
r
ith
m
s
:
n
ew
u
s
er
co
ld
-
s
tar
t
an
d
n
ew
p
ac
k
ag
e
c
o
ld
-
s
tar
t
.
T
h
ese
alg
o
r
ith
m
s
e
x
tr
ac
t
d
em
o
g
r
ap
h
ic
in
f
o
r
m
ati
o
n
s
u
ch
as
ag
e
a
n
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ased
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m
b
in
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s
o
f
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a
p
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ic
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etails.
T
h
e
s
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s
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en
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at
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ilter
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e
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esu
lts
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icate
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th
at
em
p
lo
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n
g
h
y
b
r
id
f
ilter
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alo
n
g
s
id
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d
em
o
g
r
ap
h
ic
f
ilter
in
g
is
ef
f
ec
tiv
e
in
ad
d
r
ess
in
g
th
e
co
l
d
-
s
tar
t
p
r
o
b
lem
f
o
r
n
ew
item
s
o
r
n
ew
POI
s
.
Ho
wev
er
,
it
wa
s
n
o
ted
th
at
m
an
y
d
atasets
lack
d
em
o
g
r
ap
h
ic
d
et
ails
,
wh
ich
ar
e
c
r
u
cial
f
o
r
th
e
r
ec
o
m
m
en
d
atio
n
p
r
o
ce
s
s
.
T
h
e
ch
allen
g
e
ad
d
r
ess
ed
in
t
h
e
s
tu
d
y
[
2
0
]
was
th
e
is
s
u
e
o
f
in
f
o
r
m
atio
n
o
v
er
lo
ad
,
as
u
s
er
s
f
ac
ed
d
if
f
icu
lty
s
if
tin
g
th
r
o
u
g
h
v
ast
am
o
u
n
ts
o
f
d
ata
o
n
lin
e,
wh
ic
h
co
n
s
u
m
ed
co
n
s
id
er
ab
le
tim
e.
T
o
m
itig
ate
th
is
,
th
e
au
th
o
r
s
p
r
o
p
o
s
ed
a
RS
th
at
e
m
p
lo
y
s
tag
s
an
d
co
llab
o
r
ativ
e
f
ilter
in
g
.
T
h
e
s
y
s
tem
o
p
er
ate
s
in
two
m
ain
s
tep
s
.
T
h
e
f
ir
s
t
s
tep
in
v
o
lv
es
co
n
s
tr
u
ctin
g
a
m
o
d
el
th
at
ca
p
t
u
r
e
s
to
u
r
is
ts
’
in
ter
ests
in
v
ar
io
u
s
attr
ac
tio
n
s
.
T
ag
s
ass
o
ciate
d
with
to
u
r
is
t
at
tr
ac
tio
n
s
s
er
v
e
as
f
e
atu
r
es
f
o
r
th
is
p
r
ef
er
en
ce
m
o
d
el,
an
d
a
m
u
lti
-
d
im
en
s
io
n
al
r
elatio
n
s
h
ip
am
o
n
g
to
u
r
is
ts
,
attr
ac
tio
n
tag
s
,
an
d
th
e
attr
ac
ti
o
n
s
th
em
s
elv
es
is
u
tili
ze
d
to
d
ev
elo
p
t
h
e
to
u
r
is
t
p
r
ef
er
en
ce
m
o
d
el.
T
h
e
s
ec
o
n
d
s
tep
in
v
o
lv
es
ca
lcu
latin
g
th
e
s
im
ilar
ity
b
etwe
en
to
u
r
is
t
attr
ac
tio
n
s
,
wh
ich
m
ea
s
u
r
es
h
o
w
ali
k
e
two
d
if
f
er
en
t
attr
ac
tio
n
s
a
r
e.
Fin
ally
,
a
p
e
r
s
o
n
alize
d
r
ec
o
m
m
e
n
d
a
tio
n
s
et
f
o
r
to
u
r
is
t
attr
ac
tio
n
s
is
g
en
er
ate
d
b
ased
o
n
th
e
to
u
r
is
t
’
s
in
ter
ests
an
d
th
e
ca
lcu
lated
s
im
ilar
ities
.
T
h
e
s
y
s
tem
co
m
p
u
tes
th
e
d
eg
r
ee
o
f
p
r
ef
e
r
e
n
ce
a
to
u
r
is
t
h
as
f
o
r
ea
c
h
attr
ac
tio
n
an
d
f
o
r
m
u
lates
th
e
r
ec
o
m
m
en
d
atio
n
r
es
u
lt
s
et
ac
co
r
d
i
n
g
ly
.
I
n
th
is
s
tu
d
y
[
2
1
]
,
a
d
d
r
ess
ed
th
e
lim
itatio
n
s
o
f
g
e
n
er
alize
d
p
a
ck
ag
es
o
f
f
e
r
ed
b
y
tr
av
el
ag
e
n
c
ies,
wh
ich
r
estrict
to
u
r
is
ts
’
ab
ilit
y
t
o
c
h
o
o
s
e
th
eir
p
r
e
f
er
r
ed
o
p
tio
n
s
.
T
h
ey
i
n
co
r
p
o
r
ated
co
n
ten
t
-
b
a
s
ed
f
ilter
in
g
in
th
e
r
ev
iew
m
o
d
u
le
o
f
th
eir
RS
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
is
b
u
ilt
o
n
th
r
ee
alg
o
r
ith
m
s
.
T
h
e
E
u
clid
ea
n
Dis
tan
ce
alg
o
r
ith
m
ca
lcu
lates
th
e
d
is
tan
ce
b
etwe
e
n
two
lo
ca
tio
n
s
.
th
e
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
alg
o
r
ith
m
id
en
tifie
s
th
e
n
ea
r
est
attr
ac
tio
n
s
an
d
lo
ca
tio
n
s
,
u
s
in
g
th
e
E
u
clid
ea
n
d
is
ta
n
ce
f
o
r
m
u
l
a
to
d
eter
m
in
e
wh
ich
p
lace
s
ar
e
with
in
a
s
p
ec
if
ied
r
an
g
e.
L
astl
y
,
th
e
a
p
r
i
o
r
i
alg
o
r
ith
m
is
em
p
lo
y
ed
f
o
r
class
if
icatio
n
.
T
h
e
s
y
s
tem
r
ec
o
m
m
en
d
s
n
ew
d
esti
n
at
io
n
s
to
to
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r
is
ts
,
s
u
g
g
ests
n
ea
r
b
y
attr
ac
tio
n
s
an
d
f
r
e
q
u
en
tly
v
is
ited
p
lace
s
,
p
r
o
v
id
es
r
e
v
iews
f
o
r
th
ese
lo
ca
tio
n
s
,
o
f
f
er
s
a
h
o
tel
b
o
o
k
in
g
in
ter
f
ac
e,
an
d
s
u
p
p
lies
r
o
u
te
in
f
o
r
m
atio
n
.
I
n
th
is
s
tu
d
y
[
2
2
]
,
d
ev
elo
p
ed
a
u
s
er
-
b
ased
r
ec
o
m
m
en
d
er
s
y
s
tem
f
o
r
to
u
r
is
t
attr
ac
tio
n
s
.
T
h
is
s
y
s
tem
f
u
n
ctio
n
s
as
an
o
n
lin
e
ap
p
licat
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ca
p
ab
le
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f
g
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r
atin
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n
alize
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lis
t
o
f
p
r
ef
er
r
ed
attr
ac
tio
n
s
f
o
r
to
u
r
is
ts
.
T
h
e
p
r
o
ce
s
s
in
v
o
l
v
es
th
r
ee
m
ain
s
tep
s
:
r
ep
r
esen
tin
g
u
s
er
(
t
o
u
r
is
t)
in
f
o
r
m
atio
n
,
a
n
aly
zin
g
an
d
m
o
d
elin
g
th
e
v
is
itin
g
h
is
to
r
y
o
f
attr
ac
tio
n
s
,
an
d
g
en
er
atin
g
a
lis
t
o
f
s
im
ilar
u
s
er
s
(
to
u
r
is
ts
)
.
T
h
e
s
im
ilar
ity
b
etwe
en
to
u
r
is
ts
is
ca
lcu
lated
b
ased
o
n
th
eir
v
is
itin
g
h
is
to
r
y
d
ata
u
s
in
g
a
co
llab
o
r
ativ
e
f
ilter
in
g
alg
o
r
ith
m
.
T
h
e
s
y
s
tem
th
en
g
en
er
ates r
ec
o
m
m
en
d
atio
n
s
f
o
r
th
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to
p
-
N
attr
ac
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s
tailo
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ed
to
th
e
to
u
r
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t,
in
f
o
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m
ed
b
y
th
e
v
is
itin
g
h
is
to
r
y
o
f
th
eir
n
eig
h
b
o
r
s
.
B
y
u
tili
zin
g
u
s
er
s
’
b
asic
in
f
o
r
m
atio
n
a
n
d
p
ast
tr
av
el
h
is
to
r
ies,
th
e
s
y
s
tem
id
en
tifie
s
a
lis
t
o
f
n
eig
h
b
o
r
u
s
er
s
r
ec
o
r
d
ed
in
t
h
e
u
s
er
d
atab
ase.
W
h
en
u
s
e
r
s
lo
g
in
to
th
e
s
y
s
tem
,
th
ey
r
ec
eiv
e
attr
ac
tio
n
r
ec
o
m
m
en
d
atio
n
s
b
ased
o
n
th
e
tr
av
el
ex
p
e
r
ien
ce
s
o
f
th
ei
r
n
eig
h
b
o
r
s
.
I
n
th
is
s
tu
d
y
[
2
3
]
,
cr
ea
ted
a
s
er
v
ice
with
web
an
d
m
o
b
ile
in
ter
f
ac
es
d
esig
n
ed
t
o
h
elp
u
s
er
s
d
is
co
v
er
p
r
ev
io
u
s
ly
u
n
k
n
o
wn
to
u
r
is
t
POI
.
T
h
ey
in
te
g
r
ated
a
c
o
llab
o
r
ativ
e
f
ilter
in
g
-
b
ased
r
ec
o
m
m
e
n
d
atio
n
e
n
g
in
e
th
a
t
g
en
er
ates
s
u
g
g
esti
o
n
s
b
ased
o
n
lo
ca
tio
n
s
th
e
u
s
er
h
as
r
ated
,
as
well
a
s
r
atin
g
s
f
r
o
m
o
th
er
u
s
er
s
.
T
h
e
s
er
v
ice
allo
ws u
s
er
s
to
v
is
u
alize
attr
ac
tio
n
s
n
ea
r
th
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cu
r
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en
t g
eo
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p
h
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lo
ca
tio
n
,
f
ac
ilit
ati
n
g
th
e
ef
f
o
r
tles
s
d
is
co
v
er
y
o
f
n
ew
p
lace
s
.
T
h
e
d
ev
elo
p
e
d
s
o
lu
tio
n
f
ea
tu
r
es
an
in
tu
iti
v
e
in
ter
f
ac
e
co
m
p
atib
le
with
wid
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u
s
ed
iOS
p
latf
o
r
m
s
.
Usab
ilit
y
an
d
lo
a
d
test
s
co
n
d
u
cted
o
n
th
e
ap
p
lica
tio
n
y
ield
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s
atis
f
ac
to
r
y
r
esu
lt
s
.
T
h
is
ap
p
licatio
n
aim
s
to
en
h
an
c
e
to
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r
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m
b
y
p
r
o
m
o
tin
g
a
v
ar
iety
o
f
attr
ac
tio
n
s
,
in
clu
d
in
g
less
er
-
k
n
o
wn
s
ites
,
in
th
e
v
icin
ity
o
f
th
e
u
s
er
’
s
lo
ca
tio
n
.
Me
eh
an
et
a
l
.
[
2
4
]
ad
d
r
ess
ed
is
s
u
es
r
elate
d
to
tr
ad
itio
n
al
to
u
r
g
u
id
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p
licatio
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s
,
wh
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r
im
ar
ily
f
o
cu
s
o
n
lo
ca
tio
n
wh
ile
n
eg
l
ec
tin
g
o
th
e
r
co
n
tex
tu
al
f
ac
to
r
s
.
T
h
is
lim
itatio
n
h
as
r
esu
lted
in
i
n
ap
p
r
o
p
r
iate
s
u
g
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esti
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n
s
d
u
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to
in
s
u
f
f
icien
t
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ten
t
f
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in
g
an
d
h
as led
t
o
to
u
r
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ex
p
er
ien
cin
g
i
n
f
o
r
m
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n
o
v
er
lo
ad
.
T
h
e
s
tu
d
y
em
p
lo
y
s
a
co
n
te
x
t
-
awa
r
e
h
y
b
r
id
r
ec
o
m
m
en
d
atio
n
tech
n
iq
u
e,
u
tili
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g
f
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e
m
ain
t
y
p
e
s
o
f
c
o
n
tex
tu
al
d
ata:
lo
ca
tio
n
,
tim
e,
wea
th
er
,
s
o
cial
m
ed
ia
s
en
tim
en
t,
an
d
p
e
r
s
o
n
al
izatio
n
.
B
y
i
n
co
r
p
o
r
atin
g
th
ese
d
iv
er
s
e
co
n
tex
t
u
al
f
ac
to
r
s
,
th
e
ap
p
licatio
n
y
ield
s
m
o
r
e
ac
cu
r
ate
r
esu
lts
f
o
r
u
s
er
s
.
Af
ter
ex
am
in
in
g
v
ar
io
u
s
r
ec
o
m
m
en
d
atio
n
te
ch
n
iq
u
es,
th
e
s
tu
d
y
co
n
clu
d
e
d
th
at
th
e
h
y
b
r
id
r
ec
o
m
m
en
d
atio
n
ap
p
r
o
ac
h
is
th
e
m
o
s
t
s
u
ita
b
le
s
o
lu
tio
n
f
o
r
th
e
id
en
tifie
d
r
esear
ch
p
r
o
b
lem
.
T
h
e
s
tu
d
y
[
2
5
]
in
tr
o
d
u
ce
d
co
llab
o
r
ativ
e
f
ilter
in
g
to
a
n
ew
d
o
m
ain
,
v
is
it
p
er
s
o
n
aliza
tio
n
,
u
s
in
g
a
s
im
p
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y
et
ef
f
icien
t
an
d
s
ca
lab
le
a
p
p
r
o
ac
h
.
I
t
g
ath
er
s
t
o
u
r
is
t
p
r
ef
er
en
ce
s
in
a
n
o
n
-
in
tr
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s
iv
e
m
a
n
n
er
b
y
le
v
er
ag
in
g
a
u
s
er
’
s
p
u
b
lic
tag
g
in
g
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r
d
s
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d
ev
alu
ates
to
u
r
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t
p
er
s
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n
aliza
tio
n
tech
n
iq
u
es
b
ased
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n
ac
tu
al
to
u
r
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t
ex
p
er
ien
ce
s
.
T
h
e
attr
ac
tio
n
s
a
p
er
s
o
n
ch
o
o
s
es
to
v
is
it
ar
e
in
f
lu
en
ce
d
n
o
t
o
n
ly
b
y
th
e
p
o
p
u
lar
ity
o
f
lan
d
m
ar
k
s
b
u
t a
ls
o
b
y
in
d
iv
i
d
u
al
p
r
ef
er
en
ce
s
.
T
h
e
au
th
o
r
s
an
aly
ze
t
h
e
r
ec
o
r
d
s
o
f
v
is
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lan
d
m
a
r
k
s
f
r
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m
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n
lin
e
u
s
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d
at
a
to
cr
ea
te
a
u
s
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-
u
s
er
s
im
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ity
m
atr
ix
.
W
h
en
a
u
s
er
ex
p
r
ess
es
in
ter
est
in
a
n
ew
d
esti
n
ati
o
n
,
th
e
s
y
s
tem
g
en
er
ates
a
lis
t
o
f
p
o
ten
tially
ap
p
ea
lin
g
attr
ac
tio
n
s
b
ased
o
n
th
e
ex
p
e
r
ien
ce
s
o
f
s
im
ilar
u
s
er
s
wh
o
h
av
e
alr
ea
d
y
v
is
ited
th
at
lo
ca
tio
n
.
A
k
e
y
f
i
n
d
in
g
o
f
th
e
s
tu
d
y
is
th
at
ac
cu
r
ate
p
h
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to
tag
g
in
g
en
h
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ce
s
th
e
e
x
p
e
r
i
e
n
c
e
n
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l
y
f
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t
h
e
r
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r
s
b
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t
a
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s
o
f
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t
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t
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r
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t
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s
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r
,
w
h
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f
i
t
s
f
r
o
m
p
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s
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n
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l
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d
t
o
u
r
i
s
t
r
e
c
o
m
m
e
n
d
a
t
i
o
n
s
.
T
a
b
l
e
1
p
r
o
v
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d
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s
a
s
u
m
m
a
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f
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s
f
o
c
u
s
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n
t
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u
r
i
s
m
RS
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
2
,
May
20
25
:
9
6
0
-
9
7
4
966
T
ab
le
1
.
A
s
u
m
m
a
r
y
o
f
r
ec
o
m
m
en
d
atio
n
s
y
s
tem
s
tu
d
ies
S
t
u
d
y
P
r
o
p
o
se
d
a
p
p
r
o
a
c
h
R
e
c
o
mm
e
n
d
a
t
i
o
n
t
e
c
h
n
i
q
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e
s
D
a
t
a
s
e
t
Ev
a
l
u
a
t
i
o
n
m
e
t
r
i
c
[
4
]
P
e
r
so
n
a
l
i
z
e
d
r
e
c
o
mm
e
n
d
a
t
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s
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m
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d
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ser
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s
p
r
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s,
d
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s
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y
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n
d
d
i
s
t
a
n
c
e
.
C
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b
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t
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n
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Tr
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ser rat
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g
s
a
n
d
met
a
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t
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o
f
P
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[
1
2
]
Ev
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e
p
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d
t
r
a
v
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r
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c
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m
me
n
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sy
st
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m
C
o
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t
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n
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-
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a
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d
f
i
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r
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o
l
l
a
b
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r
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t
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v
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f
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l
t
e
r
i
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g
_
P
e
r
f
o
r
ma
n
c
e
me
t
r
i
c
s
P
r
e
d
i
c
t
i
o
n
q
u
a
l
i
t
y
met
r
i
c
s
R
a
n
k
i
n
g
met
r
i
c
s
[
1
3
]
R
e
c
o
mm
e
n
d
a
t
i
o
n
s
y
st
e
m
u
si
n
g
S
e
ma
n
t
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c
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l
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st
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r
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n
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n
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se
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me
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t
a
n
a
l
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s
U
ser
-
b
a
se
d
f
i
l
t
e
r
i
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A
d
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t
a
s
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g
a
t
h
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r
e
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f
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o
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Tr
i
p
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d
v
i
s
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p
l
a
t
f
o
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i
mi
l
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r
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t
y
me
t
r
i
c
S
y
mm
e
t
r
i
c
ma
t
r
i
x
[
1
4
]
F
r
a
mew
o
r
k
d
e
s
i
g
n
f
o
r
p
e
r
so
n
a
l
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z
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c
o
m
me
n
d
a
t
i
o
n
s
o
f
o
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l
i
n
e
t
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r
i
sm
C
o
l
l
a
b
o
r
a
t
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v
e
f
i
l
t
e
r
i
n
g
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o
n
t
e
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t
-
b
a
se
d
_
A
n
a
l
y
s
i
s
o
f
v
a
r
i
o
u
s
a
c
c
u
r
a
c
y
me
t
r
i
c
s
[
1
5
]
I
n
c
o
r
p
o
r
a
t
i
o
n
o
f
u
ser
r
e
v
i
e
w
s
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n
t
o
t
o
u
r
i
s
t
RS
.
C
o
n
t
e
n
t
-
b
a
se
d
Eu
r
o
p
e
h
o
t
e
l
s
_
[
1
6
]
C
o
n
t
e
x
t
u
a
l
i
n
f
o
r
m
a
t
i
o
n
f
o
r
RS
H
y
b
r
i
d
f
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l
t
e
r
i
n
g
F
o
u
r
s
q
u
a
r
e
LC
S
[
1
8
]
G
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
a
n
d
LS
TM
n
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k
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st
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h
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p
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l
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r
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sm s
e
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s c
o
n
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s
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r
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n
f
l
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e
w
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t
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c
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a
l
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t
w
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k
s a
n
d
t
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ser
i
e
s
d
a
t
a
.
C
o
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a
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o
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t
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R
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l
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A
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s
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c
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a
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me
t
r
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c
s
[
1
9
]
H
y
b
r
i
d
r
e
c
o
mm
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n
d
a
t
i
o
n
b
a
se
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e
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D
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P
O
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s
U
ser si
m
i
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a
r
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[
2
0
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P
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ser si
m
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t
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[
2
1
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R
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c
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mm
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m
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si
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r
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g
_
R
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c
a
l
l
m
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r
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c
[
2
2
]
O
n
l
i
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p
p
l
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c
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t
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t
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To
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ser
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b
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g
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U
ser si
m
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l
a
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t
y
[
2
3
]
To
u
r
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st
g
u
i
d
e
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n
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g
r
a
t
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d
w
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me
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m t
h
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a
mo
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g
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sers.
C
o
l
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a
b
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f
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l
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M
o
v
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e
Le
n
s
M
e
a
n
a
b
so
l
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t
e
e
r
r
o
r
[
2
4
]
C
o
n
t
e
x
t
-
a
w
a
r
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r
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c
o
mm
e
n
d
a
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m
H
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b
r
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t
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f
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t
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r
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n
g
_
[
2
5
]
P
e
r
so
n
a
l
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z
e
d
r
e
c
o
mm
e
n
d
a
t
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s
y
st
e
m
d
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v
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l
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p
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d
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r
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g
h
t
h
e
a
n
a
l
y
si
s
o
f
so
c
i
a
l
me
d
i
a
d
a
t
a
.
C
o
l
l
a
b
o
r
a
t
i
v
e
f
i
l
t
e
r
i
n
g
F
l
i
c
k
r
u
sers
U
ser si
m
i
l
a
r
i
t
y
m
a
t
r
i
x
Fro
m
o
u
r
r
ev
iew,
we
ca
n
d
r
aw
th
e
f
o
llo
win
g
co
n
clu
s
io
n
s
:
−
Mo
s
t
s
tu
d
ies
h
av
e
co
n
ce
n
tr
ated
o
n
co
llab
o
r
ativ
e
f
ilter
in
g
as
th
e
p
r
im
a
r
y
r
ec
o
m
m
e
n
d
atio
n
t
ec
h
n
iq
u
e.
T
h
is
ap
p
r
o
ac
h
is
d
r
iv
en
b
y
th
e
n
o
tio
n
th
at
u
s
er
s
ten
d
to
r
ec
eiv
e
th
e
b
est
r
ec
o
m
m
en
d
atio
n
s
f
r
o
m
o
t
h
er
s
wh
o
s
h
ar
e
s
im
ilar
p
r
ef
er
en
ce
s
.
Fu
r
th
e
r
m
o
r
e,
co
llab
o
r
ativ
e
f
ilter
in
g
en
ab
les
th
e
m
o
d
el
to
ass
is
t
u
s
er
s
in
d
is
co
v
er
in
g
n
ew
in
ter
ests
with
o
u
t r
eq
u
ir
in
g
ex
ten
s
iv
e
d
o
m
ain
k
n
o
wled
g
e.
−
C
er
tain
s
tu
d
ies,
in
clu
d
i
n
g
[
1
6
]
,
[
1
9
]
,
an
d
[
2
4
]
,
h
a
v
e
e
x
p
lo
r
e
d
h
y
b
r
id
f
ilter
in
g
tech
n
iq
u
es.
T
h
ey
in
c
o
r
p
o
r
ated
co
n
tex
tu
al
in
f
o
r
m
atio
n
,
s
u
ch
as
g
eo
g
r
ap
h
ical
an
d
tem
p
o
r
al
d
ata,
to
en
h
an
ce
a
n
d
ass
ess
th
e
q
u
ality
o
f
r
ec
o
m
m
en
d
atio
n
s
.
−
On
ly
th
e
s
tu
d
y
[
2
0
]
f
o
cu
s
ed
o
n
in
teg
r
atin
g
co
llab
o
r
ativ
e
f
ilt
er
in
g
with
tag
g
in
g
.
W
e
b
eliev
e
th
at
attr
ac
tio
n
tag
s
allo
w
to
u
r
is
ts
to
ex
p
r
ess
th
eir
o
p
in
io
n
s
a
b
o
u
t
v
ar
i
o
u
s
a
ttra
ctio
n
s
.
T
h
u
s
,
th
ese
tag
s
s
er
v
e
as
a
cr
u
cial
lin
k
b
etwe
en
to
u
r
is
ts
an
d
t
o
u
r
is
t
s
ites
,
p
r
o
v
id
in
g
v
al
u
ab
le
d
ata
to
a
d
d
r
ess
th
eir
in
ter
ests
.
E
ac
h
to
u
r
is
t
ca
n
tag
m
u
ltip
le
attr
ac
tio
n
s
s
im
u
ltan
eo
u
s
ly
,
an
d
th
e
c
o
llectio
n
o
f
tag
s
ass
ig
n
ed
b
y
ea
c
h
to
u
r
is
t
ca
n
r
ev
ea
l
th
ei
r
p
r
ef
er
en
ce
s
a
n
d
h
o
b
b
ies.
4.
P
RO
P
O
SE
D
RE
CO
M
M
E
N
DATI
O
N
SYS
T
E
M
T
h
is
s
tu
d
y
in
tr
o
d
u
ce
s
a
p
er
s
o
n
alize
d
to
u
r
is
m
RS
s
p
ec
if
ically
d
esig
n
ed
f
o
r
th
e
Qass
im
r
eg
io
n
in
th
e
Kin
g
d
o
m
o
f
Sau
d
i
Ar
ab
ia.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
u
tili
ze
s
a
tag
-
b
ased
a
p
p
r
o
ac
h
c
o
m
b
in
e
d
with
co
llab
o
r
ativ
e
f
ilter
in
g
tech
n
i
q
u
es
to
r
ec
o
m
m
en
d
to
u
r
is
m
o
p
tio
n
s
in
v
ar
io
u
s
cities
with
in
Qass
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.
I
t
en
co
m
p
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es
all
p
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an
d
POI
s
in
th
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r
eg
io
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p
r
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r
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m
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ased
o
n
u
s
er
s
’
s
to
r
e
d
p
r
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e
r
en
ce
s
.
T
h
is
s
y
s
tem
en
ab
les
to
u
r
is
ts
to
p
lan
th
eir
tr
ip
s
m
o
r
e
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f
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t
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wh
ile
m
in
im
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tim
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d
e
f
f
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r
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in
d
lo
ca
tio
n
s
th
at
alig
n
with
th
eir
i
n
ter
ests
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
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J
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4
.
1
.
Sy
s
t
e
m
m
e
t
ho
do
lo
g
y
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
co
m
p
r
is
es
two
p
r
im
ar
y
co
m
p
o
n
en
ts
.
F
ig
u
r
e
2
d
e
p
icts
th
e
o
v
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all
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et
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B
elo
w,
we
p
r
o
v
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a
d
etailed
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tio
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Fig
u
r
e
2
.
Pro
p
o
s
ed
m
o
d
u
les f
o
r
th
e
r
ec
o
m
m
en
d
atio
n
s
y
s
te
m
4
.
1
.
1
.
User
re
g
is
t
ra
t
io
n mo
du
le
I
n
th
is
m
o
d
u
le,
u
s
er
s
ar
e
r
eq
u
ir
ed
to
cr
ea
te
a
n
ac
co
u
n
t
with
in
th
e
s
y
s
tem
.
Du
r
in
g
th
e
r
eg
is
tr
atio
n
p
r
o
ce
s
s
,
th
ey
m
u
s
t
p
r
o
v
id
e
p
e
r
s
o
n
al
d
etails
in
clu
d
in
g
th
eir
n
am
e,
d
ate
o
f
b
ir
t
h
,
ad
d
r
ess
,
p
h
o
n
e
n
u
m
b
er
,
em
ail
ad
d
r
ess
,
an
d
p
ass
wo
r
d
.
T
o
en
s
u
r
e
u
s
er
p
r
iv
ac
y
,
t
h
is
in
f
o
r
m
at
io
n
will b
e
s
to
r
ed
in
a
n
en
cr
y
p
ted
d
atab
ase.
On
ce
t
h
e
r
e
g
i
s
t
r
a
t
i
o
n
i
s
c
o
m
p
l
e
t
e
,
u
s
e
r
s
c
a
n
l
o
g
i
n
t
o
t
h
e
s
y
s
t
e
m
.
U
s
e
r
r
e
g
i
s
t
r
a
t
i
o
n
m
o
d
u
l
e
i
n
c
l
u
d
e
s
t
h
e
f
o
l
l
o
w
i
n
g
s
t
e
p
s
:
-
User
lo
g
in
:
w
h
en
a
u
s
er
attem
p
ts
to
lo
g
in
,
th
e
y
a
r
e
p
r
o
m
p
ted
to
en
ter
th
eir
u
s
er
n
am
e/em
ai
l
an
d
p
ass
wo
r
d
.
I
f
th
e
cr
ed
e
n
tials
m
atch
,
th
e
u
s
er
g
ain
s
ac
ce
s
s
to
th
e
s
y
s
tem
;
i
f
n
o
t,
an
e
r
r
o
r
m
ess
ag
e
is
d
is
p
l
ay
ed
,
p
r
o
m
p
tin
g
th
em
to
r
etr
y
.
-
Data
r
etr
iev
al:
u
p
o
n
s
u
cc
ess
f
u
l
lo
g
in
,
th
e
s
y
s
tem
r
etr
iev
es
th
e
u
s
er
’
s
p
r
o
f
ile,
in
clu
d
in
g
th
eir
s
to
r
ed
p
r
ef
er
en
ce
s
,
h
is
to
r
ical
d
ata,
a
n
d
a
n
y
tag
s
ass
o
ciate
d
with
th
eir
p
ast
in
ter
ac
tio
n
s
.
T
h
is
d
ata
is
ess
en
tial
f
o
r
g
en
er
atin
g
p
er
s
o
n
alize
d
r
ec
o
m
m
en
d
atio
n
s
.
-
Secu
r
ity
m
ea
s
u
r
es:
all
p
er
s
o
n
a
l
in
f
o
r
m
atio
n
,
in
clu
d
in
g
p
ass
wo
r
d
s
,
is
s
to
r
ed
in
an
e
n
cr
y
p
ted
f
o
r
m
at
to
p
r
o
tect
u
s
er
p
r
iv
ac
y
.
T
h
is
m
o
d
u
le
is
d
esig
n
ed
to
p
r
o
v
id
e
u
s
er
s
with
a
s
ec
u
r
e
an
d
u
s
er
-
f
r
ien
d
l
y
in
ter
f
ac
e
f
o
r
ac
c
ess
in
g
an
d
m
an
a
g
in
g
th
eir
p
er
s
o
n
al
in
f
o
r
m
atio
n
,
en
s
u
r
in
g
th
at
th
eir
d
ata
is
b
o
t
h
a
cc
ess
ib
le
an
d
p
r
o
tecte
d
.
B
y
f
a
cilitatin
g
ea
s
y
lo
g
in
an
d
d
ata
m
an
ag
e
m
en
t,
th
e
m
o
d
u
le
p
lay
s
a
v
ital
r
o
le
in
en
h
a
n
cin
g
u
s
er
ex
p
e
r
ien
ce
an
d
e
n
g
ag
em
en
t
with
th
e
to
u
r
is
m
RS
.
4
.
1
.
2
.
Rec
o
mm
enda
t
io
n t
ec
h
niq
ue
s
a
nd
t
a
g
m
o
du
le
I
n
th
is
m
o
d
u
le,
to
u
r
is
ts
u
tili
z
e
attr
ac
tio
n
tag
s
to
ex
p
r
ess
th
eir
o
p
in
io
n
s
ab
o
u
t
v
ar
io
u
s
to
u
r
is
t
s
ites
.
T
h
ese
tag
s
s
er
v
e
as
a
co
n
n
ec
ti
o
n
b
etwe
en
to
u
r
is
ts
an
d
attr
ac
tio
n
s
,
p
r
o
v
id
in
g
v
al
u
ab
le
d
ata
to
ca
ter
t
o
to
u
r
is
ts
’
in
ter
ests
.
E
ac
h
to
u
r
is
t
ca
n
tag
m
u
ltip
le
attr
ac
tio
n
s
s
im
u
ltan
eo
u
s
ly
.
T
h
e
c
o
llectio
n
o
f
tag
s
ass
ig
n
ed
b
y
ea
c
h
to
u
r
is
t
ca
n
r
ev
ea
l
th
eir
p
r
ef
e
r
en
ce
s
an
d
h
o
b
b
ies.
I
n
th
is
m
o
d
u
le,
f
i
r
s
t,
th
e
u
s
er
’
s
b
eh
av
io
r
an
d
h
is
to
r
ical
b
r
o
wsi
n
g
r
ec
o
r
d
s
with
in
th
e
s
y
s
tem
will
b
e
an
aly
ze
d
.
B
ased
o
n
th
is
d
ata,
a
t
o
u
r
is
t
-
tag
m
atr
ix
will
b
e
cr
ea
ted
to
d
ev
elo
p
an
in
ter
est
m
o
d
el
f
o
r
to
u
r
is
ts
r
eg
ar
d
in
g
v
ar
io
u
s
att
r
ac
tio
n
s
,
ac
h
iev
ed
b
y
co
n
s
tr
u
ctin
g
a
tag
-
to
u
r
is
t
attr
ac
tio
n
m
atr
ix
.
T
h
is
m
atr
ix
will
b
e
b
u
ilt
u
s
in
g
th
e
s
et
o
f
a
ttra
ctio
n
tag
s
.
T
o
co
m
p
u
te
th
e
s
im
ilar
ity
b
etwe
en
to
u
r
is
t
attr
ac
tio
n
s
,
it
is
n
o
ted
th
at
d
if
f
er
e
n
t
to
u
r
is
ts
m
ay
ass
ig
n
v
ar
i
o
u
s
tag
s
to
th
e
s
am
e
attr
ac
tio
n
s
.
T
h
e
g
r
ea
ter
th
e
n
u
m
b
e
r
o
f
tag
s
ass
ig
n
ed
to
an
attr
ac
tio
n
,
th
e
m
o
r
e
ch
ar
ac
ter
is
tics
o
f
th
at
attr
ac
tio
n
ca
n
b
e
r
e
p
r
esen
ted
,
wh
ich
will
b
e
ca
lcu
lated
u
s
in
g
a
s
im
ilar
ity
m
etr
ic.
L
astl
y
,
a
t
o
p
-
n
r
ec
o
m
m
e
n
d
atio
n
lis
t
will
b
e
g
en
e
r
ated
b
ased
o
n
th
e
to
u
r
is
t in
ter
est m
o
d
el
a
n
d
th
e
s
im
ilar
ity
am
o
n
g
th
e
attr
ac
tio
n
s
.
R
ec
o
m
m
en
d
atio
n
tec
h
n
iq
u
es a
n
d
t
ag
m
o
d
u
le
i
n
clu
d
es th
e
f
o
l
lo
win
g
s
tep
s
:
-
User
b
eh
av
io
r
a
n
aly
s
is
:
th
e
s
y
s
tem
an
aly
ze
s
u
s
er
b
eh
av
i
o
r
a
n
d
h
is
to
r
ical
b
r
o
wsi
n
g
r
ec
o
r
d
s
to
u
n
d
er
s
tan
d
how
to
u
r
is
ts
in
ter
ac
t
with
d
if
f
er
en
t
attr
ac
tio
n
s
.
T
h
is
an
aly
s
is
h
elp
s
id
en
tify
p
atter
n
s
in
ta
g
g
in
g
b
eh
a
v
io
r
,
r
ev
ea
lin
g
wh
ich
ty
p
es o
f
attr
a
ctio
n
s
r
eso
n
ate
m
o
s
t w
ith
p
ar
ti
cu
lar
u
s
er
g
r
o
u
p
s
.
-
T
o
u
r
is
t
-
tag
m
atr
ix
cr
ea
tio
n
:
b
a
s
ed
o
n
th
e
co
llected
tag
s
,
a
to
u
r
is
t
-
ta
g
m
atr
ix
is
cr
ea
ted
to
m
o
d
el
th
e
in
ter
ests
o
f
ea
c
h
to
u
r
is
t
in
r
elatio
n
to
v
ar
io
u
s
attr
ac
tio
n
s
.
T
h
is
m
atr
i
x
r
ef
lects
h
o
w
f
r
eq
u
en
tly
an
d
wh
ich
tag
s
ar
e
ass
ig
n
ed
to
ea
ch
attr
ac
tio
n
b
y
d
if
f
er
en
t to
u
r
is
ts
.
-
T
ag
-
to
u
r
is
t
attr
ac
tio
n
m
atr
ix
:
t
h
e
s
y
s
tem
co
n
s
tr
u
cts
a
tag
-
to
u
r
is
t
attr
ac
tio
n
m
atr
ix
th
at
m
ap
s
tag
s
to
s
p
ec
if
ic
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
2
,
May
20
25
:
9
6
0
-
9
7
4
968
attr
ac
tio
n
s
.
T
h
is
m
atr
ix
p
r
o
v
i
d
es
in
s
ig
h
t
in
to
th
e
co
llectiv
e
in
ter
ests
o
f
to
u
r
is
ts
,
en
ab
lin
g
m
o
r
e
tailo
r
e
d
r
ec
o
m
m
en
d
atio
n
s
.
T
o
en
h
a
n
c
e
th
e
r
ec
o
m
m
en
d
atio
n
p
r
o
ce
s
s
,
th
e
s
y
s
tem
ca
lcu
lates
th
e
s
i
m
ilar
it
y
b
etwe
en
attr
ac
tio
n
s
b
ased
o
n
th
e
tag
s
ass
ig
n
ed
b
y
d
if
f
er
e
n
t
to
u
r
is
ts
.
Attr
ac
tio
n
s
with
a
g
r
ea
ter
n
u
m
b
er
o
f
tag
s
ar
e
b
etter
r
ep
r
esen
ted
in
ter
m
s
o
f
th
eir
ch
ar
ac
ter
is
tics
,
allo
win
g
f
o
r
m
o
r
e
ac
cu
r
ate
c
o
m
p
ar
is
o
n
s
.
-
T
o
p
-
n
r
ec
o
m
m
en
d
atio
n
lis
t:
f
in
ally
,
a
to
p
-
n
r
ec
o
m
m
en
d
ati
o
n
lis
t
is
g
e
n
er
ated
u
s
in
g
th
e
to
u
r
is
t
in
ter
est
m
o
d
el
an
d
t
h
e
ca
lcu
lated
s
im
ilar
ities
b
etwe
en
attr
ac
tio
n
s
.
T
h
is
lis
t
p
r
o
v
id
es
to
u
r
is
ts
with
p
er
s
o
n
alize
d
s
u
g
g
esti
o
n
s
th
at
alig
n
with
th
eir
in
ter
ests
an
d
p
r
ef
er
en
ce
s
,
en
h
an
cin
g
th
eir
ex
p
e
r
ien
ce
an
d
in
cr
ea
s
in
g
th
e
lik
elih
o
o
d
o
f
ex
p
lo
r
atio
n
.
T
h
is
m
o
d
u
le
lev
er
a
g
es
u
s
er
-
g
e
n
er
ated
co
n
te
n
t
to
cr
ea
te
a
d
y
n
am
ic
an
d
r
esp
o
n
s
iv
e
RS
th
at
ca
ter
s
to
th
e
d
iv
er
s
e
in
ter
ests
o
f
to
u
r
is
ts
.
4
.
2
.
Sy
s
t
e
m
d
esig
n
A
d
ata
f
lo
w
d
ia
g
r
am
(
DFD)
h
as b
ee
n
u
tili
ze
d
to
o
u
tlin
e
th
e
ar
ch
itectu
r
e
o
f
th
e
p
r
o
p
o
s
ed
RS
.
Fig
u
r
e
3
illu
s
tr
ates
th
e
s
y
s
tem
d
esig
n
,
wh
ich
will b
e
ex
p
lain
e
d
in
d
et
ail
b
elo
w.
T
h
e
co
m
p
o
n
en
ts
o
f
a
DFD
in
c
lu
d
e
th
e
f
o
llo
win
g
:
−
E
x
ter
n
al
e
n
titi
es
:
th
ese
r
ep
r
es
en
t
u
s
er
s
,
s
u
ch
as
to
u
r
is
ts
wh
o
in
ter
ac
t
with
th
e
s
y
s
tem
.
T
h
ey
p
r
o
v
id
e
in
p
u
t
(
s
u
ch
as p
er
s
o
n
al
in
f
o
r
m
atio
n
an
d
attr
ac
tio
n
ta
g
s
)
an
d
r
ec
eiv
e
r
ec
o
m
m
en
d
atio
n
s
.
−
Pro
ce
s
s
es:
th
e
m
ain
f
u
n
ctio
n
s
o
f
th
e
s
y
s
tem
,
s
u
c
h
as
u
s
er
r
eg
is
tr
atio
n
,
tag
in
p
u
t,
attr
ac
ti
o
n
tag
g
i
n
g
,
a
n
d
r
ec
o
m
m
en
d
atio
n
g
en
er
atio
n
,
ar
e
d
ep
icted
as
p
r
o
ce
s
s
es.
E
ac
h
p
r
o
c
ess
tr
an
s
f
o
r
m
s
th
e
i
n
p
u
t
d
ata
i
n
to
m
ea
n
in
g
f
u
l o
u
tp
u
t.
−
Data
s
to
r
es
:
th
ese
in
d
icate
wh
e
r
e
th
e
s
y
s
tem
’
s
d
ata
is
s
to
r
ed
,
i
n
clu
d
in
g
u
s
er
p
r
o
f
iles
,
attr
ac
tio
n
tag
s
,
an
d
th
e
T
o
u
r
is
t
-
tag
m
atr
ix
.
Dat
a
s
to
r
es a
r
e
cr
u
cial
f
o
r
m
ain
tain
i
n
g
h
i
s
to
r
ical
r
ec
o
r
d
s
an
d
u
s
er
p
r
ef
e
r
en
ce
s
.
−
Data
f
lo
ws
:
ar
r
o
ws
illu
s
tr
ate
h
o
w
d
ata
m
o
v
es
b
etwe
en
ex
t
er
n
al
en
titi
es,
p
r
o
ce
s
s
es,
an
d
d
ata
s
to
r
es.
T
h
is
v
is
u
al
r
ep
r
esen
tatio
n
h
elp
s
clar
if
y
h
o
w
in
f
o
r
m
atio
n
is
g
ath
er
ed
,
p
r
o
ce
s
s
ed
,
an
d
u
s
ed
to
g
en
er
ate
r
ec
o
m
m
en
d
atio
n
s
.
B
y
an
aly
zin
g
th
e
DFD,
we
ca
n
g
ain
in
s
ig
h
ts
in
to
th
e
in
ter
ac
ti
o
n
s
with
in
th
e
RS
an
d
u
n
d
e
r
s
tan
d
h
o
w
u
s
er
in
p
u
t
lead
s
to
tailo
r
ed
s
u
g
g
esti
o
n
s
f
o
r
to
u
r
is
t a
ttra
ctio
n
s
.
T
h
e
u
s
er
r
eg
is
tr
atio
n
an
d
lo
g
in
m
o
d
u
le
f
u
n
c
tio
n
s
as
a
c
r
u
cial
co
m
p
o
n
en
t
o
f
th
e
p
r
o
p
o
s
ed
p
e
r
s
o
n
alize
d
to
u
r
is
m
RS
.
Her
e
’
s
a
d
etailed
b
r
ea
k
d
o
wn
o
f
its
o
p
er
atio
n
s
:
wh
en
a
u
s
er
attem
p
ts
to
lo
g
in
,
th
e
u
s
er
r
eg
is
tr
atio
n
an
d
lo
g
in
m
o
d
u
le
r
etr
iev
es
th
e
ir
cr
ed
en
tials
,
in
clu
d
in
g
th
eir
u
s
er
n
am
e/em
ail
an
d
p
ass
wo
r
d
,
f
r
o
m
t
h
e
d
atab
ase
cr
ea
ted
d
u
r
in
g
th
eir
in
itial
r
e
g
i
s
tr
atio
n
.
Ad
d
itio
n
ally
,
u
s
er
s
h
a
v
e
th
e
o
p
tio
n
to
u
p
d
ate
t
h
eir
in
f
o
r
m
atio
n
,
s
u
ch
as
p
ass
wo
r
d
s
,
em
ail
ad
d
r
ess
es,
an
d
p
er
s
o
n
al
d
etails.
T
h
e
s
y
s
tem
also
m
o
n
ito
r
s
th
e
u
s
er
’
s
p
r
ev
io
u
s
b
r
o
wsi
n
g
ac
ti
v
ity
an
d
b
eh
av
i
o
r
to
s
u
p
p
o
r
t
t
h
e
tag
an
d
r
ec
o
m
m
en
d
atio
n
m
o
d
u
les.
T
h
is
d
ata
will
b
e
u
s
ed
to
d
ev
el
o
p
th
e
to
u
r
is
t
-
tag
f
r
am
ewo
r
k
,
wh
ich
an
aly
ze
s
t
h
e
lev
el
o
f
i
n
ter
est
th
at
to
u
r
is
ts
d
is
p
lay
in
tr
av
el
-
r
elate
d
attr
ac
tio
n
s
.
A
tag
-
to
u
r
is
t
attr
ac
tio
n
m
atr
i
x
will
b
e
co
n
s
tr
u
cted
u
s
in
g
t
h
e
s
et
o
f
at
tr
ac
tio
n
tag
s
.
T
h
e
s
im
ilar
ity
o
f
to
u
r
is
t
attr
ac
tio
n
s
ca
n
b
e
ass
ess
ed
,
as
d
if
f
er
en
t
v
is
ito
r
s
m
ay
ass
ig
n
v
ar
i
o
u
s
t
ag
s
to
th
e
s
am
e
attr
ac
tio
n
.
T
h
e
m
o
r
e
tag
s
ass
ig
n
ed
t
o
an
attr
ac
tio
n
,
th
e
m
o
r
e
f
ea
tu
r
es
ca
n
b
e
r
ep
r
esen
ted
,
an
d
s
i
m
ilar
ity
m
etr
ics
will
f
ac
il
it
ate
th
is
an
aly
s
is
.
F
in
ally
,
a
to
p
-
n
r
ec
o
m
m
e
n
d
atio
n
lis
t w
ill b
e
g
en
er
ated
b
ased
o
n
th
e
to
u
r
is
t in
ter
est m
o
d
el
a
n
d
th
e
ca
lcu
lated
s
im
ilar
ities
am
o
n
g
th
e
attr
ac
tio
n
s
.
Fig
u
r
e
3
.
Sy
s
tem
d
esig
n
u
s
in
g
d
ata
f
lo
w
d
iag
r
am
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
ta
g
-
b
a
s
ed
r
ec
o
mme
n
d
er sys
tem
fo
r
to
u
r
is
m
u
s
in
g
co
lla
b
o
r
a
tive
filt
erin
g
(
A
fef
S
elmi)
969
5.
SYST
E
M
EV
AL
UA
T
I
O
N
5
.
1
.
Da
t
a
c
o
llect
io
n
T
o
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
RS
,
it
is
cr
u
cial
to
u
tili
ze
a
r
ea
l
d
ataset.
T
h
er
ef
o
r
e,
w
e
d
ev
elo
p
e
d
a
d
ataset
s
p
ec
if
ical
ly
f
o
r
th
e
Qass
im
r
eg
io
n
,
wh
i
ch
in
clu
d
es
v
ar
i
o
u
s
f
iles
c
o
n
t
ain
in
g
in
f
o
r
m
atio
n
ab
o
u
t
u
s
er
s
a
n
d
d
if
f
er
en
t
attr
a
ctio
n
s
.
T
h
is
d
ataset
en
co
m
p
ass
es
d
etails
o
n
:
i)
u
s
er
in
f
o
r
m
a
tio
n
,
ii)
a
ttra
ctio
n
s
,
iii)
c
h
alets
,
iv
)
h
er
itag
e
lan
d
m
ar
k
s
,
v
)
p
ar
k
s
,
v
i)
r
estau
r
an
ts
,
v
ii)
p
u
b
lic
p
lace
s
,
v
iii)
m
alls
,
an
d
ix
)
h
o
tels
T
h
e
d
ata
was
g
ath
er
e
d
f
r
o
m
m
u
ltip
le
s
o
u
r
ce
s
,
i
n
clu
d
in
g
G
o
o
g
le
Ma
p
s
an
d
I
n
s
tag
r
a
m
,
e
n
s
u
r
in
g
a
co
m
p
r
eh
e
n
s
iv
e
r
e
p
r
esen
tatio
n
o
f
t
h
e
r
e
g
io
n
’
s
o
f
f
er
i
n
g
s
.
T
h
is
r
ich
d
ataset
will
en
a
b
le
u
s
to
an
aly
ze
th
e
ef
f
ec
tiv
en
ess
o
f
t
h
e
RS
an
d
its
ab
ilit
y
to
ca
ter
t
o
u
s
er
p
r
ef
e
r
en
ce
s
an
d
in
ter
ests
.
So
m
e
s
am
p
le
s
f
r
o
m
th
e
g
ath
e
r
ed
d
ataset
ar
e
illu
s
tr
ated
in
th
e
f
o
llo
win
g
f
o
r
m
ats:
−
T
ab
le
2
p
r
o
v
id
es
a
d
escr
ip
tio
n
o
f
th
e
attr
ac
t
io
n
f
ile,
s
h
o
wca
s
in
g
v
ar
io
u
s
attr
ib
u
tes
an
d
d
etails
r
elev
an
t
to
th
e
attr
ac
tio
n
s
in
th
e
Qass
im
r
eg
io
n
.
−
Fig
u
r
e
4
d
is
p
lay
s
a
s
am
p
le
o
f
t
h
e
ch
alets
,
h
ig
h
lig
h
tin
g
k
ey
in
f
o
r
m
atio
n
s
u
c
h
as
lo
ca
tio
n
,
am
e
n
ities
,
an
d
u
s
er
r
atin
g
s
.
−
Fig
u
r
e
5
p
r
esen
ts
a
s
am
p
le
o
f
h
e
r
itag
e
lan
d
m
ar
k
s
,
o
f
f
e
r
in
g
in
s
ig
h
ts
in
to
th
eir
h
is
to
r
i
ca
l
s
ig
n
if
ican
ce
,
f
ea
tu
r
es,
an
d
v
is
ito
r
in
f
o
r
m
atio
n
.
T
h
ese
r
ep
r
esen
tatio
n
s
will
h
el
p
in
u
n
d
er
s
tan
d
in
g
th
e
s
tr
u
ctu
r
e
an
d
co
n
ten
t
o
f
t
h
e
d
ataset
u
s
ed
f
o
r
d
e
v
elo
p
in
g
th
e
RS
.
T
ab
le
2
.
Attr
ac
tio
n
d
ataset
’
s
attr
ib
u
tes an
d
th
eir
d
escr
ip
tio
n
s
A
t
t
r
i
b
u
t
e
n
a
me
A
t
t
r
i
b
u
t
e
d
e
scri
p
t
i
o
n
C
a
t
e
g
o
r
y
C
l
a
s
si
f
i
c
a
t
i
o
n
o
f
a
t
t
r
a
c
t
i
o
n
s f
o
r
sp
e
c
i
f
i
c
c
a
t
e
g
o
r
i
e
s
Ti
t
l
e
A
t
t
r
a
c
t
i
o
n
s
’
t
i
t
l
e
s
U
R
L
Li
n
k
t
o
a
l
l
a
t
t
r
a
c
t
i
o
n
s
i
n
t
h
e
G
o
o
g
l
e
M
a
p
a
p
p
l
i
c
a
t
i
o
n
I
t
e
m I
D
I
t
e
m I
D
a
ssi
g
n
s
p
e
c
i
a
l
i
d
f
o
r
e
a
c
h
i
t
e
m
Fig
u
r
e
4
.
Sam
p
le
o
f
th
e
Qass
i
m
r
eg
io
n
ch
alets
Fig
u
r
e
5
.
Sam
p
le
o
f
Qass
im
r
e
g
io
n
h
e
r
itag
e
lan
d
m
a
r
k
s
5
.
2
.
Da
t
a
p
re
pro
ce
s
s
ing
T
o
en
s
u
r
e
th
e
ac
cu
r
ac
y
an
d
q
u
ality
o
f
th
e
d
ata,
d
ata
p
r
ep
r
o
ce
s
s
in
g
is
es
s
en
tial.
T
h
is
s
ec
ti
o
n
o
u
tlin
es
th
e
v
ar
io
u
s
s
tep
s
in
v
o
lv
ed
in
th
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
p
h
ase.
T
h
e
au
to
m
atic
clea
n
in
g
p
r
o
ce
s
s
es
ar
e
ca
r
r
ied
o
u
t
u
s
in
g
Py
th
o
n
co
d
e,
an
d
t
h
ese
s
tep
s
ca
n
b
e
s
u
m
m
ar
ize
d
as f
o
l
lo
ws:
−
C
h
ec
k
in
g
n
u
ll
v
alu
es
a
n
d
d
r
o
p
I
n
s
tag
r
am
ac
co
u
n
t
:
in
t
h
is
s
tag
e,
we
c
h
ec
k
n
u
ll v
alu
es
f
r
o
m
th
e
d
ataset
a
n
d
we
d
r
o
p
I
n
s
tag
r
am
ac
c
o
u
n
ts
f
r
o
m
th
e
r
ec
o
r
d
s
as sh
o
wn
in
Fig
u
r
e
6
.
−
R
em
o
v
in
g
r
ec
o
r
d
s
wi
th
m
is
s
in
g
v
alu
es:
a
f
ter
c
h
ec
k
in
g
n
u
ll
v
alu
es,
we
r
em
o
v
e
th
e
r
ec
o
r
d
s
wh
er
e
th
e
r
eg
io
n
is
m
is
s
in
g
in
th
e
d
ata
f
r
am
e
as
s
h
o
wn
in
Fig
u
r
e
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.