INTELLIGENT TECHNIQUES FOR WEB PERSONALIZATION RECOMMENDER SYSTEMS
Patterns for Personalization on the Web
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Transcript of Patterns for Personalization on the Web
Semantic Patterns for Web Personalization
Lora Aroyo [email protected]
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
Application Domains @ VU Amsterdam
what’s interesting for me in the museum?
Artwork Recommendations & Personalized museum guide
http://chip-‐project.org
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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)
http://www.cs.vu.nl/~laroyo twitter: @laroyo
enriched rijksmuseum collection
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
semantic recommendations
semantic artwork presentation
semantic explanations
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)
http://www.cs.vu.nl/~laroyo twitter: @laroyo
11 semantic relationships
http://www.cs.vu.nl/~laroyo twitter: @laroyo
4 artwork features
• link an artwork & its associated concepts – The Jewish Bride (Artwork) –creator-‐> Rembrandt (ULAN)
– The Jewish Bride (Artwork) –crea3onSite-‐> Amsterdam (TGN)
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
navigation patterns • artwork -‐> creator -‐> style -‐> broader/narrower styles • artwork -‐> creator -‐> teacher/student -‐> styles • artwork -‐> subject -‐> broader/narrower subjects
artwork
http://www.cs.vu.nl/~laroyo twitter: @laroyo
what to watch tonight?
Personalized Program Guide with Social Web Activities http://notube.tv
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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)
http://www.cs.vu.nl/~laroyo twitter: @laroyo
enrichment of TV metadata
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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
LOD is BIG & MESSY
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
to filter out the noise in LOD …
we look for patterns in LOD
to improve performance of semantic search
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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 http://www.cs.vu.nl/~laroyo twitter: @laroyo
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!
http://www.cs.vu.nl/~laroyo twitter: @laroyo
where do we use all this …
for recommendations of content
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
finding interesting relations
• deep links • related info • granularity of content
– for discussion – for user feedback
http://www.cs.vu.nl/~laroyo twitter: @laroyo
distributed context
© danbri http://www.cs.vu.nl/~laroyo
cross-‐domain recommendations
• domain independent content patterns
• context (in-‐)dependency
• cross-‐application
• cross-‐domain
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
relevance to the user?
© danbri http://www.cs.vu.nl/~laroyo
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)
http://www.cs.vu.nl/~laroyo twitter: @laroyo
User Profile schema: capture user context & temporal changes
User Modelling: (Social) Web user activity & user preference data
user profiling -‐ activity streams
http://www.cs.vu.nl/~laroyo twitter: @laroyo
NoTube BeanCounter: aggregating & profiling
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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
http://www.cs.vu.nl/~laroyo twitter: @laroyo
NOTUBE DEMONSTRATORS
© Libby Miller, BBC
• http://vimeo.com/10553773 • http://vimeo.com/11232681
http://notube.tv
NoTube Demonstrator I: Personalized Semantic News
http://www.cs.vu.nl/~laroyo twitter: @laroyo
OnlineTV Guide SeAop Box EPG
NoTube Demonstrator II: Personalized EPG & Ads
Mobile Iden3ty
• ID Anywhere • No3fica3ons
• Synchroniza3on with STB • Seman3c Search
• My TV Night • What’s on for me • Related Programs
http://ifanzy.nl
http://www.cs.vu.nl/~laroyo twitter: @laroyo
NoTube Demonstrator III: Social TV & Web
• http://vimeo.com/10553773 • http://vimeo.com/11232681
http://www.cs.vu.nl/~laroyo twitter: @laroyo
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 team • RAI team
http://www.cs.vu.nl/~laroyo twitter: @laroyo