Personalization on the Web with Semantic Patterns (in LOD)

Web Personalization with Semantic Patterns (in LOD) Lora Aroyo [email_address] 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 data patterns , e.g.
domain-specific
representation-specific
alignment-based
combine semantics with user context
determine user relevance and ranking
generate meaningful explanations
select suitable presentation

semantics and content http://e-culture.multimedian.nl/software/ClioPatria.shtml

users, semantics and content © danbri

distributed context © danbri

breaking walls - building bridges
Distributed, Web-based multimedia collections
Semantic Culture Web
MultimediaN E-Culture: cultural search engine
Europeana: the EU Culture portal
CHIP: personalized museums
Semantic Television
iFanzy, NoTube, PrestoPrime
annotation and access to TV archives
personalized integration of TV and Web content

use case 1: 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 specialization
ARIA database: 729 artworks; 47,329 triples
Adlib database: 16,156 artworks; 400,405 triples
Vocabularies
RM Dictionary (#486), RM Encyclopaedia (#690), RM Catalogue (#43)
Getty TGN (#425,517), Getty ULAN (#1,896,936), Getty AAT(#1,249,162), IconClass (# 24349)
(Manual) Alignments
~4000 alignts.: ARIA to ~750 concepts (Getty and IconClass)
(AdLib) to ~4500 concepts (Getty)

enriched rijksmuseum collection

style: Baroque teacher of: Gerrit Dou teacher of: Nicolaes Maes teacher of: Ferdinand Bol self-portrait militia place: Amsterdam, 1625 to 1650

what can we do with semantics?
Generate automatically (personalized) tours
adapt tours on the fly
combine spatial, temporal & semantic constraints
Generate automatically recommendations
cluster & classify
related artworks
related art/history concepts
boost the ‘interestingness’ & ‘serendipity’ factors
Generate automatically explanations

semantic recommendations

artwork features
link an artwork & its associated concepts
The Jewish Bride (Artwork ) –creator-> Rembrandt (ULAN)
The Jewish Bride (Artwork) –creationSite-> Amsterdam (TGN)
in total 4 artwork features were identified

semantic relationships
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)
in total 11 semantic relationship were identified

semantic relationships

results …
vra:creator & link:hasStyle & aat:broader/narrower
most accurate recommendations & most interesting to users
ulan:birth/deathPlace & tgn: broader/narrower
have the least values for accuracy and interestingness
vra:subject & (subject) skos:broader/narrower
highest recall for recommended concepts & resulted in most user ratings
accuracy and interestingness, they score average

Good Bad Average

navigation patterns
artwork -> creator -> style -> broader/narrower styles
artwork -> creator -> teacher/student -> styles
artwork -> subject -> broader/narrower subjects
artwork

Multiple Vocabularies & LOD

issues to explore 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
user likes ``Rembrandt”- the system could recommend artworks related to his death place ``Amsterdam" or even the broader geographic location ``Noord-Holland”

use case 2: what to watch tonight? Personalized Program Guide with Social Web Activities http://notube.tv

deciding what to watch is difficult

Social Web: (isolated) data silos

FOAF (Friend-of-a-Friend)

user profiling - activity streams

weighted user interests

NoTube BeanCounter: aggregating & profiling

simple event model

enrichment of EPG metadata

semantics & linked data in TV
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 EPG metadata enrichment
Publish NoTube additions as extension to LOD
Combine and align Web & TV standards (public broadcasters)

semantics & linked data @ BBC
BBC Programs and BBC Music ensure ONE page per programme (artist) with RDF representation
BBC Program Ontology
BBC Wildlife Finder provides a URI for every species, habitat and adaption
The BBC’s World Cup site uses RDF and Linked Data for a site of 700 aggregation pages

finding interesting relations
Deep links
Ontology Patterns
Interestingness factor
Serendipity factor
Related info
Allows proper discussion
Different contexts

cross-domain recommendations
domain independent content patterns
context (in-)dependency
cross-application
cross-domain

recommendation trends
Use of social media
Twitter TV trends amongst my friends
What my friends are watching
What's most popular on Twitter right now
What my friends/celebrities are liking on FB
Hunch.com links between content and people stereotypes
Push personalization
Filter automatically irrelevant content
Push relevant background content
Reduce the burden of too much choice
Surface programs of interest in the ‘long tail’, support (interesting) content discovery, serendipity, knowledge building

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

NoTube Demonstrator I: Personalized Semantic News

NoTube Demonstrator II: Personalized EPG & Ads OnlineTV Guide Settop Box EPG Mobile Identity
ID Anywhere
Notifications
Synchronization with STB
Semantic Search
My TV Night
What’s on for me
Related Programs
http://ifanzy.nl

NoTube Demonstrator III: Social TV & Web

Acknowledgements & Image Credits
Libby Miller, BBC
Vicky Buser, BBC
Dan Brickley, VUA
Guus Schreiber, VUA
Natalia Stash, TUe
Yiwen Wang, TUe
Peter Gorgels, RMA
http://pidgintech.com
Stoneroos
RAI

IMAGE CREDITS
http://pidgintech.com
Dan Brickley, VU Amsterdam
Libby Miller, BBC
Vicky Buser, BBC
Stoneroos

Personalization on the Web with Semantic Patterns (in LOD)

by Lora Aroyo

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