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


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 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)


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

artwork


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


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


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 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!


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


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


NoTube Demonstrator III: Social TV & Web

• http://vimeo.com/10553773 • http://vimeo.com/11232681


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


patterns for personalization on the web

Technology

concepts ge4y http

semantic relationships

netherlands http

average http

dicult http

semantic patterns

enrichment of tv metadata

broadernarrower subjects