Patterns for Personalization on the Web

Semantic
Patterns
for
Web

Personalization

Lora
Aroyo

l.m.aroyo@cs.vu.nl

Web
&
Media
Group

Faculty
of
Computer
Science

VU
University
Amsterdam,
The
Netherlands

http://www.cs.vu.nl/~laroyo twitter: @laroyo

the
personalization
challenge

• discover
useful
linked
(open)
data
pa4erns

– domain-‐speciﬁc

– representa8on-‐speciﬁc

– alignment-‐based

• combine
seman8cs
with
user
context

• determine
user
relevance
and
ranking

• generate
meaningful
explana8ons

• select
suitable
presenta8on


Application
Domains

@
VU
Amsterdam

what’s
interesting
for
me
in
the

museum?

Artwork
Recommendations
&

Personalized
museum
guide

http://chip-‐project.org


museum
metadata
&
vocabularies

• Metadata
format
is
Dublin-‐Core
specializa8on

– ARIA
database:
729
artworks;
47,329
triples

– Adlib
database:
16,156
artworks;
400,405
triples

• Vocabularies

– RM
Dic8onary
(#486),
RM
Encyclopaedia
(#690),
RM
Catalogue
(#43)

– Ge4y
TGN
(#425,517),
Ge4y
ULAN
(#1,896,936),
Ge4y
AAT
(#1,249,162),
IconClass
(#
24349)

• (Manual)
Alignments

– ~4000
alignts.:
ARIA
to
~750
concepts
(Ge4y
and
IconClass)

– (AdLib)
to
~4500
concepts
(Ge4y)


enriched
rijksmuseum
collection


what
can
we
do
with
semantics?

• Generate
automa8cally
(personalized)
tours

– adapt
tours
on
the
ﬂy

– combine
spa8al,
temporal
&
seman8c
constraints

• Generate
automa8cally
recommenda3ons

– cluster
&
classify

– related
artworks

– related
art/history
concepts

– boost
the
‘interes8ngness’
&
‘serendipity’
factors

• Generate
automa8cally
explana3ons


semantic
artwork
presentation

how
did
we
start
…

WordNet
patterns
for
query

expansion

patterns
of
semantic
relations
in

WordNet

• Hollink,
et.
Al
(2007)

11
semantic
relationships

• Wang,
et
al
(2009a,
2009b)

• link
two
art
concepts
within
one
vocabulary
or

across
two
diﬀerent
vocabularies,
e.g.

– Rembrandt
(ULAN)
–studentOf-‐>
Pieter
Lastman
(ULAN)

– Rembrandt
(ULAN)
–hasStyle-‐>
Baroque
(AAT)

– Rembrandt
(ULAN)
–deathPlace-‐>
Amsterdam
(TGN)


11
semantic
relationships


4
artwork
features

• link
an
artwork
&
its
associated
concepts

– The
Jewish
Bride
(Artwork)
–creator-‐>
Rembrandt
(ULAN)

– The
Jewish
Bride
(Artwork)
–crea3onSite-‐>
Amsterdam
(TGN)


results
…

• vra:creator
&
link:hasStyle

&
aat:broader/narrower

– most
accurate

recommenda8ons
&
most

interes8ng
to
users

• ulan:birth/deathPlace
&

tgn:
broader/narrower

– have
the
least
values
for

accuracy
and
interes8ngness

• vra:subject
&
(subject)

skos:broader/narrower

– highest
recall
for

recommended
concepts
&

resulted
in
most
user
ra8ngs

– accuracy
and
interes8ngness,

they
score
average


navigation
patterns

•

artwork
-‐>
creator
-‐>
style
-‐>
broader/narrower
styles

•

artwork
-‐>
creator
-‐>
teacher/student
-‐>
styles

•

artwork
-‐>
subject
-‐>
broader/narrower
subjects


what
to
watch
tonight?

Personalized
Program
Guide
with

Social
Web
Activities

http://notube.tv


deciding
what
to
watch
is
diﬃcult

http://www.cs.vu.nl/~laroyo

Can
Linked
Data
Help?

can
linked
open
data
help?

ﬁrst
we
…

• select
media-‐related
Linked
Data

• semantically
enrich
TV
program
metadata

• deﬁne
similarity
measures
for
TV
programs

• semantic
content-‐based
recommendations

TV-‐related
linked
data

• DBPedia,
Freebase,
WordNet(s)

• TV
genre
typologies,
IMDB,
TV
Anytime,
BBC

Programme
ontology,
(constantly
growing
list)

• Expose
TV
metadata
as
Semantic
Web
data

• Use
LOD
concepts
for
TV
metadata
enrichment

• Publish
NoTube
additions
as
extension
to
LOD

• Combine
and
align
Web
&
TV
standards
(public

broadcasters)


enrichment
of
TV
metadata


semantics
&
linked
data
@
BBC

• BBC
Programs
and
BBC
Music
ensure
ONE

page
per
programme
(ar8st)
with
RDF

representa8on

• BBC
Program
Ontology

• BBC
Wildlife
Finder
provides
a
URI
for
every

species,
habitat
and
adap8on

• The
BBC’s
World
Cup
site
uses
RDF
and
Linked

Data
for
a
site
of
700
aggrega8on
pages


LOD
is
BIG
&
MESSY

many
interesting
facts

but
also
much
straight
forward
knowledge,

e.g.
“Peter
Jackson
is
a
human
being”
is
necessary,

but
a
trivial
fact
from
a
user’s
perspective

source
for
noise
in
LOD
…

• Multiple
(large)
vocabularies
with
various

semantics

• Multiple
alignments
between
vocabularies

Content-‐based
recommendations
with
a
wide

range
of
concepts

• Not
all
semantically
related
concepts
are

interesting
for
end
users


to
ﬁlter
out
the
noise
in
LOD
…

we
look
for

patterns
in
LOD

to
improve
performance
of
semantic
search


how
did
we
do
it
…

• select
the
appropriate
LOD
sources

– detect
representative
knowledge
patterns

– Identify
pattern
types
–
higher
recall/similar

precision

• generic
patterns,
i.e.
hierarchical
&
associative

• speciﬁc
patterns
-‐
less
applicable,
but
rendering

better
performance
than
generic
patterns

– enrich
the
data
according
to
those
patterns

• extract
all
possible
pathway
patterns


method

• List
of
all
Properties
(P)
as
deﬁned
in
their

vocabulary
(with
domain
and
range)

• P
Statistics
-‐
#
triples
that
use
it,
universes
and
%

of
use
of
subject
&
object
types

• Align
P
to
top-‐level
P
in
general
Content
ODPs

– mappings
-‐

owl:equivalentProperty,

rdfs:subPropertyOf

• Align
P
universes
to
top-‐level
classes
in
ODPs

• Identify
paths


paths

• ordered
list
of
properties
from
triple
sequences

that
instantiate
the
path

– a
length
(min
2)
=
#
properties
that
compose
it

– a
number
of
occurrences
=
#
of
its
instances
in
dataset

• Property
has

position
in
path,
subject
and
object
types

– linkedmdb:cinematographer,
linkedmdb:performance,
linkedmdb:film_character!


where
do
we
use
all
this
…

for
recommendations
of
content


recommendations
with
patterns

• reduce
the
burden
of
too
much
choice

– ﬁlter
out
irrelevant
items

– push
relevant
background
items

– surface
programs
of
interest
in
the
‘long
tail’

• support

– (interesting)
content
discovery

– serendipity

– knowledge
building


ﬁnding
interesting
relations

• deep
links

• related
info

• granularity
of
content

– for
discussion

– for
user
feedback


cross-‐domain
recommendations

• domain

independent

content

patterns

• context
(in-‐)
dependency

• cross-‐
application

• cross-‐domain


generating
explanations

• Help
users
to:

– learn
the
recommendation

mechanisms

– understand
why
something

is
recommended

– quicker
share

recommended
content

– give
better
feedback
to
the

recommender
engine


next
we
…

• select
only
the
LOD
pa4erns
that
match

relevance
for
a
given
user
e.g.
using
the
user

proﬁle
&
context

• ﬁnd
rela8ons
between
a
user
and
program

– interes8ngness
factor

– serendipity
factor

– context
factor,
e.g.
8me,
loca8on,
device

FOAF
(Friend-‐of-‐a-‐Friend)

User
Proﬁle
schema:

capture
user
context
&
temporal
changes

User
Modelling:

(Social)
Web
user
activity
&
user
preference
data


user
proﬁling
-‐
activity
streams


NoTube
BeanCounter:

aggregating
&
proﬁling


patterns
in
social
media

• Twitter
TV
trends
in
people
I
follow

– what
my
friends
are
watching

– what's
most
popular
on
Twitter
right
now

– what
my
celebrities
are
liking
on
FB

• Hunch.com
links
between
content
&
people

stereotypes


NoTube
Demonstrator
I:

Personalized
Semantic
News


NoTube
Demonstrator
II:

Personalized
EPG
&
Ads

OnlineTV
Guide
SeAop
Box
EPG
Mobile
Iden3ty

•
Synchroniza3on
with
STB

• My
TV
Night

• ID
Anywhere

•
Seman3c
Search

• What’s
on
for
me

• No3ﬁca3ons

• Related
Programs

http://ifanzy.nl


NoTube
Demonstrator
III:

Social
TV
&
Web


Acknowledgements
&

Image
Credits

• Libby
Miller,
BBC
• http://pidgintech.com

• Vicky
Buser,
BBC
• Stoneroos
team

• Dan
Brickley,
VUA
• RAI
team

• Guus
Schreiber,
VUA

• Natalia
Stash,
TUe

• Yiwen
Wang,
TUe

• Peter
Gorgels,
RMA


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Editor's Notes