Semantic Associations in Recommender Systems: Expanding Personalization, Study notes of Computer Science

Download Semantic Associations in Recommender Systems: Expanding Personalization and more Study notes Computer Science in PDF only on Docsity! ICAI Review James Michaelis CSCI 6965 — Adv. Semantic Technologies (Class 7) 3/3/2009 Semantic Reasoning: A Path To New Possibilities of Personalization. Strategy
 •  Two
key
parts:
 –  Seman:c
associa:ons
 •  Trace
seman:c
bonds
between
the
user’s
preferences
and
 the
items
available
in
the
recommender
system
 •  Formalize
these
bonds
in
a
domain
ontology,
along
with
 their
seman:c
annota:ons
 –  Spreading
Ac:va:on
techniques
 •  Explore
these
seman:c
rela:onships
and
discover
new
 knowledge
related
to
the
users’
interests.
 •  Domain
for
demonstra:on:
Movie/TV
Reviewing
 Seman:c
Associa:ons
 •  Domain
knowledge
is
encoded
in
ontology
 •  In
turn,
users
can
specify
interest
in
certain
movies,
using
 parameter
DegreesOfInterest
(con:nuous
variable
ranging
 from
1
to
‐1)
 •  With
user
preferences
entered,
traversal
through
domain
 ontology
takes
place
 •  Relevance
Index
computed
through
following
metrics:
 –  
ρ‐path
associa:on:
Applies
for
two
programs
linked

by
a
chain
 or
sequence
of
proper:es
in
the
ontology.
 –  
ρ‐join
associa:on:
Two
programs
with
respec:ve
akributes
 belonging
to
the
same
class
in
the
domain
ontology.
 –  
ρ‐cp
associa:on:
Two
programs
sharing
a
common
ancestor
in
 the
genre
hierarchy
defined
in
the
ontology.

 Semantic Associations _ ee a anCaeecr Semantic Associations —_ — a _ p-path (Jerry Maguire, Born on 4th July) cae ae on, p-join (Welcome to Tokyo, The Last Samurai) we RL f/orN p-join (Learn about WW I, Born on 4th July) Come comme — <7 sain (camer) Cm p-ep (Vanilla Sky, The Last Samurai) , “ C Poe p-cp (Danny the Dog, Game of Death) SS ) cc. a> ce. ae p-path (Danny the Dog, Million Dollar Baby) wwii, : \\atsouire ) + Seam Sy) Samara i — Ne para) (Woteoma ihe Dog Dollar Baby 4th of July ‘Shut of Glery te Tokyo Toyo Kyoto er = Kung Fu Kamte a 1D, Karate 10, atic ag MasTepie 1D, aes ‘Dollar Baby. Has Actor Raney Teties Bace Lee Morgan he Dog > Game of Freeman / Actorin Hastopic_( Come Hasactor Kung Fu Wor Nicole Aaem seat —"\_ Var, }-—— Drecormn — HasActressy Kidman WW ner rope nt otGiony —e ‘Stantey >, Eyes wide os Saaieeee nis 1D, ) Masdeector Haaren "Otsd fo Tearyo, Tetyo on Aetoan Tom 1D, rata —_—_— rabid Ath of duly * Cruise. HasActor if a HasTopc ae SY L nals 0 Jey — — Maguire | vaspwecter 54 Kyoto ee 1D, 1. )*rarsa Ip, ) "ie (Cameran ‘4 HasDirector Evalua:on
 •  Authors
implemented
prototype
of
recommender
 system
for
tes:ng
purposes
including:
 – An
OWL
ontology
covering
the
domain
of
TV
shows

 •  Similar
to
the
movie
ontology
the
technology
was
developed
 around
 – User
modeling
technique
based
on
ontology‐profiles
 •  Goal
of
study
was
to:
 –  To
evaluate
the
accuracy
of
our
reasoning‐based
 recommenda:ons
 –  To
compare
this
approach
with
two
exis:ng
machine‐ learning
techniques
that
are
devoid
of
seman:c
 inference
capabili:es
used
by
authors.
 Evalua:on
 •  400
Students
asked
to
rate
400
TV
programs
on
 con:nuous
scale
of
[‐1,1]
 •  40
%
of
student
evalua:ons
used
as
training
data
 for
the
two
machine
learning
techniques
 •  For
remaining
60%
of
students,
the
following
 took
place:
 –  10
good,
and
10
bad
TV
shows
randomly
selected
 from
each
student
evalua:on
to
build
profile
 –  In
turn,
these
profiles
were
fed
into
the
authors
 recommenda:ons
system,
as
well
as
the
other
two
 systems
 90 80 70 60 50 40 30 20 10 Results C0 Asso-SA (threshold = 0.65) O Rules [| i sem-cF lt 10 8 6 — 4 2 o 2 2 Precision Review
of
Paper
 •  Significance
of
Results:
8/10
 –  Interes:ng
comparison
done
between
the
authors
 system
and
two
alternate
systems
 – However,
it
is
not
par:cularly
clear
why
these
two
 were
selected
(a
likle
more
background
on
 selec:on
ra:onale
would
be
helpful)
 –  In
addi:on,
expanding
the
pool
of
par:cipants
and
 or
domain
data
in
a
follow‐up
evalua:on
would
be
 helpful
(an
inten:on
the
authors
expressed
as
 future
work).
 Review
of
Paper
 •  Technical
Soundness:
8/10
 –  Specifics
of
some
techniques
(such
as
those
used
to
 compute
significance
of
proper:es
related
to
movies
 rated
by
users)
omiked
from
paper
due
to
length
 constraints
 •  However,
a
thorough
technical
review
of
all
the
 techniques
used
in
this
system
given
in
Blanco
Y.
 et
al.

 – A
Flexible
seman:c
inference
methodology
to
reason
 about
user
preferences
in
knowledge
based
 recommender
systems.
Knowledge‐based
Systems,
In
 press.
 Review
of
Paper
 •  Quality
of
Evalua:on:
6/10
 – Two
metrics
used
for
ra:ng
effec:veness
of
three
 techniques
 •  Recall:
Defined
as
the
percent
of
returned
programs
 that
the
user
rated
interes:ng
 •  Precision:
Defined
as
percent
of
programs
returned
 with
posi:ve
user
ra:ng
on
[‐1,1]
scale
 – Not
very
well
defined
what
dis:nguishes
these
 metrics
in
the
results
 – Expanded
sta:s:cal
analysis
would
be
helpful