Journal of Robotics and Automation Research,
Год журнала:
2023,
Номер
4(2)
Опубликована: Июль 3, 2023
This
survey
discusses
the
concept
of
knowledge
graphs,
including
their
construction,
extraction,
and
applications.Various
tools
such
as
Zotero,
Web
Science,
Google
Scholar,
EndNote,
VosViewer
are
used
to
analyze
visualize
collected
data.A
Boolean
query
mechanism
ensures
gathered
material
is
relevant
study.The
discussion
includes
studies
on
relation
extraction
using
graph
neural
networks,
application
graphs
in
biomedical
research,
use
embedding
healthcare.These
highlight
growing
importance
managing
representing
complex
information.Notable
discussed
include
role
connecting
related
medical
information,
technology
healthcare,
potential
benefits
limitations
data
analysis.This
paper
provides
valuable
insights
into
information
how
they
can
help
provide
new
various
fields.It
suggests
future
directions
for
research
this
area,
highlighting
continued
exploration
innovation
realize
fully.
Numerous
goods
and
services
are
now
offered
through
online
platforms
due
to
the
recent
growth
of
transactions
like
e-commerce.
Users
have
trouble
locating
product
that
best
suits
them
from
numerous
products
available
in
shopping.
Many
studies
deep
learning-based
recommender
systems
(RSs)
focused
on
intricate
relationships
between
attributes
users
items.
Deep
learning
techniques
used
consumer
or
item-related
traits
improve
quality
personalized
many
areas,
such
as
tourism,
news,
Various
companies,
primarily
e-commerce,
utilize
sentiment
analysis
enhance
effectively
navigate
today's
business
environment.
Customer
feedback
regarding
a
is
gathered
analysis,
which
uses
contextual
data
split
it
into
separate
polarities.
The
explosive
rise
e-commerce
industry
has
resulted
large
body
literature
different
perspectives.
Researchers
made
an
effort
categorize
recommended
future
possibilities
for
study
field
grown.
There
several
challenges
fake
reviews,
frequency
user
advertisement
click
fraud,
code-mixing.
In
this
review,
we
introduce
overview
preliminary
design
Second,
concept
learning,
discussed.
Third,
represent
versions
commercial
dataset.
Finally,
explain
various
difficulties
facing
RS
research
directions.
Future Internet,
Год журнала:
2023,
Номер
15(10), С. 323 - 323
Опубликована: Сен. 28, 2023
Traditional
movie
recommendation
systems
are
increasingly
falling
short
in
the
contemporary
landscape
of
abundant
information
and
evolving
user
behaviors.
This
study
introduced
temporal
knowledge
graph
recommender
system
(TKGRS),
a
ground-breaking
algorithm
that
addresses
limitations
existing
models.
TKGRS
uniquely
integrates
convolutional
networks
(GCNs),
matrix
factorization,
decay
factors
to
offer
robust
dynamic
mechanism.
The
algorithm’s
architecture
comprises
an
initial
embedding
layer
for
identifying
item,
followed
by
GCN
nuanced
understanding
relationships
fully
connected
layers
prediction.
A
factor
is
also
used
give
weightage
recent
user–item
interactions.
Empirical
validation
using
MovieLens
100K,
1M,
Douban
datasets
showed
outperformed
state-of-the-art
models
according
evaluation
metrics,
i.e.,
RMSE
MAE.
innovative
approach
sets
new
standard
opens
avenues
future
research
advanced
algorithms
machine
learning
techniques.
