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 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 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

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 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

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

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

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 – whats 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 Demonstrator I: Personalized Semantic News http://www.cs.vu.nl/~laroyo twitter: @laroyo

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

NoTube Demonstrator III: Social TV & Web • http://vimeo.com/10553773 • http://vimeo.com/11232681http://www.cs.vu.nl/~laroyo twitter: @laroyo

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

Patterns for Personalization on the Web

by Lora Aroyo on Jan 24, 2011

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Patterns for Personalization on the Web — Presentation Transcript