Personalization on the Web with Semantic Patterns (in LOD)

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Presentation given at STLab at CNR, Rome
Starting a collaboration for identifying useful knowledge and navigation patterns in LOD to recommend media (TV programs and News) content

20 Sept 2010
http://stlab.istc.cnr.it/stlab/STLab:News/12

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Personalization on the Web with Semantic Patterns (in LOD)

  1. 1. 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
  2. 2. The personalization challenge <ul><li>discover useful linked data patterns , e.g. </li></ul><ul><ul><li>domain-specific </li></ul></ul><ul><ul><li>representation-specific </li></ul></ul><ul><ul><li>alignment-based </li></ul></ul><ul><li>combine semantics with user context </li></ul><ul><li>determine user relevance and ranking </li></ul><ul><li>generate meaningful explanations </li></ul><ul><li>select suitable presentation </li></ul>
  3. 3. semantics and content http://e-culture.multimedian.nl/software/ClioPatria.shtml
  4. 4. users, semantics and content © danbri
  5. 5. distributed context © danbri
  6. 6. breaking walls - building bridges <ul><li>Distributed, Web-based multimedia collections </li></ul><ul><li>Semantic Culture Web </li></ul><ul><ul><li>MultimediaN E-Culture: cultural search engine </li></ul></ul><ul><ul><li>Europeana: the EU Culture portal </li></ul></ul><ul><ul><li>CHIP: personalized museums </li></ul></ul><ul><li>Semantic Television </li></ul><ul><ul><li>iFanzy, NoTube, PrestoPrime </li></ul></ul><ul><ul><li>annotation and access to TV archives </li></ul></ul><ul><ul><li>personalized integration of TV and Web content </li></ul></ul>
  7. 7. use case 1: what’s interesting for me in the museum? Artwork Recommendations & Personalized museum guide http://chip-project.org
  8. 8. museum metadata & vocabularies <ul><li>Metadata format is Dublin-Core specialization </li></ul><ul><ul><li>ARIA database: 729 artworks; 47,329 triples </li></ul></ul><ul><ul><li>Adlib database: 16,156 artworks; 400,405 triples </li></ul></ul><ul><li>Vocabularies </li></ul><ul><ul><li>RM Dictionary (#486), RM Encyclopaedia (#690), RM Catalogue (#43) </li></ul></ul><ul><ul><li>Getty TGN (#425,517), Getty ULAN (#1,896,936), Getty AAT(#1,249,162), IconClass (# 24349) </li></ul></ul><ul><li>(Manual) Alignments </li></ul><ul><ul><li>~4000 alignts.: ARIA to ~750 concepts (Getty and IconClass) </li></ul></ul><ul><ul><li>(AdLib) to ~4500 concepts (Getty) </li></ul></ul>
  9. 9. enriched rijksmuseum collection
  10. 10. style: Baroque teacher of: Gerrit Dou teacher of: Nicolaes Maes teacher of: Ferdinand Bol self-portrait militia place: Amsterdam, 1625 to 1650
  11. 11. what can we do with semantics? <ul><li>Generate automatically (personalized) tours </li></ul><ul><ul><li>adapt tours on the fly </li></ul></ul><ul><ul><li>combine spatial, temporal & semantic constraints </li></ul></ul><ul><li>Generate automatically recommendations </li></ul><ul><ul><li>cluster & classify </li></ul></ul><ul><ul><li>related artworks </li></ul></ul><ul><ul><li>related art/history concepts </li></ul></ul><ul><ul><li>boost the ‘interestingness’ & ‘serendipity’ factors </li></ul></ul><ul><li>Generate automatically explanations </li></ul>
  12. 12. semantic recommendations
  13. 13. artwork features <ul><li>link an artwork & its associated concepts </li></ul><ul><ul><li>The Jewish Bride (Artwork ) –creator-> Rembrandt (ULAN) </li></ul></ul><ul><ul><li>The Jewish Bride (Artwork) –creationSite-> Amsterdam (TGN) </li></ul></ul><ul><ul><li>in total 4 artwork features were identified </li></ul></ul>
  14. 14. semantic relationships <ul><li>link two art concepts within one vocabulary or across two different vocabularies, e.g. </li></ul><ul><ul><li>Rembrandt (ULAN) –studentOf-> Pieter Lastman (ULAN) </li></ul></ul><ul><ul><li>Rembrandt (ULAN) –hasStyle-> Baroque (AAT) </li></ul></ul><ul><ul><li>Rembrandt (ULAN) –deathPlace-> Amsterdam (TGN) </li></ul></ul><ul><ul><li>in total 11 semantic relationship were identified </li></ul></ul>
  15. 15. semantic relationships
  16. 16. results … <ul><li>vra:creator & link:hasStyle & aat:broader/narrower </li></ul><ul><ul><li>most accurate recommendations & most interesting to users </li></ul></ul><ul><li>ulan:birth/deathPlace & tgn: broader/narrower </li></ul><ul><ul><li>have the least values for accuracy and interestingness </li></ul></ul><ul><li>vra:subject & (subject) skos:broader/narrower </li></ul><ul><ul><li>highest recall for recommended concepts & resulted in most user ratings </li></ul></ul><ul><ul><li>accuracy and interestingness, they score average </li></ul></ul>
  17. 17. Good Bad Average
  18. 18. navigation patterns <ul><li>artwork -> creator -> style -> broader/narrower styles </li></ul><ul><li>artwork -> creator -> teacher/student -> styles </li></ul><ul><li>artwork -> subject -> broader/narrower subjects </li></ul>artwork
  19. 19. Multiple Vocabularies & LOD
  20. 20. Multiple Vocabularies & LOD
  21. 21. issues to explore in LOD … <ul><li>Multiple (large) vocabularies with various semantics </li></ul><ul><li>Multiple alignments between vocabularies Content-based recommendations with a wide range of concepts </li></ul><ul><li>Not all semantically related concepts are interesting for end users </li></ul><ul><ul><li>user likes ``Rembrandt”- the system could recommend artworks related to his death place ``Amsterdam&quot; or even the broader geographic location ``Noord-Holland” </li></ul></ul>
  22. 22. use case 2: what to watch tonight? Personalized Program Guide with Social Web Activities http://notube.tv
  23. 23. deciding what to watch is difficult
  24. 24. Social Web: (isolated) data silos
  25. 25. FOAF (Friend-of-a-Friend)
  26. 26. user profiling - activity streams
  27. 27. weighted user interests
  28. 28. NoTube BeanCounter: aggregating & profiling
  29. 29. simple event model
  30. 31. enrichment of EPG metadata
  31. 33. semantics & linked data in TV <ul><li>DBPedia, Freebase, WordNet(s), TV genre typologies, IMDB, TV Anytime, BBC Programme ontology, (constantly growing list) </li></ul><ul><li>Expose TV metadata as Semantic Web data </li></ul><ul><li>Use LOD concepts for EPG metadata enrichment </li></ul><ul><li>Publish NoTube additions as extension to LOD </li></ul><ul><li>Combine and align Web & TV standards (public broadcasters) </li></ul>
  32. 34. semantics & linked data @ BBC <ul><li>BBC Programs and BBC Music ensure ONE page per programme (artist) with RDF representation </li></ul><ul><li>BBC Program Ontology </li></ul><ul><li>BBC Wildlife Finder provides a URI for every species, habitat and adaption </li></ul><ul><li>The BBC’s World Cup site uses RDF and Linked Data for a site of 700 aggregation pages </li></ul>
  33. 35. finding interesting relations <ul><li>Deep links </li></ul><ul><li>Ontology Patterns </li></ul><ul><li>Interestingness factor </li></ul><ul><li>Serendipity factor </li></ul><ul><li>Related info </li></ul><ul><li>Allows proper discussion </li></ul><ul><li>Different contexts </li></ul>
  34. 36. cross-domain recommendations <ul><li>domain independent content patterns </li></ul><ul><li>context (in-)dependency </li></ul><ul><li>cross-application </li></ul><ul><li>cross-domain </li></ul>
  35. 37. recommendation trends <ul><li>Use of social media </li></ul><ul><ul><li>Twitter TV trends amongst my friends </li></ul></ul><ul><ul><li>What my friends are watching </li></ul></ul><ul><ul><li>What's most popular on Twitter right now </li></ul></ul><ul><ul><li>What my friends/celebrities are liking on FB </li></ul></ul><ul><ul><li>Hunch.com links between content and people stereotypes </li></ul></ul><ul><li>Push personalization </li></ul><ul><ul><li>Filter automatically irrelevant content </li></ul></ul><ul><ul><li>Push relevant background content </li></ul></ul><ul><ul><li>Reduce the burden of too much choice </li></ul></ul><ul><ul><li>Surface programs of interest in the ‘long tail’, support (interesting) content discovery, serendipity, knowledge building </li></ul></ul>
  36. 38. generating explanations <ul><li>Help users to: </li></ul><ul><ul><li>Learn the recommendation mechanisms </li></ul></ul><ul><ul><li>Understand why something is recommended </li></ul></ul><ul><ul><li>Quicker share recommended content </li></ul></ul><ul><ul><li>Give better feedback to the recommender engine </li></ul></ul>
  37. 39. NOTUBE DEMONSTRATORS © Libby Miller, BBC <ul><li> http://vimeo.com/10553773 </li></ul><ul><li> http://vimeo.com/11232681 </li></ul>http://notube.tv
  38. 40. NoTube Demonstrator I: Personalized Semantic News
  39. 41. NoTube Demonstrator II: Personalized EPG & Ads OnlineTV Guide Settop Box EPG Mobile Identity <ul><li>ID Anywhere </li></ul><ul><li>Notifications </li></ul><ul><li>Synchronization with STB </li></ul><ul><li>Semantic Search </li></ul><ul><li>My TV Night </li></ul><ul><li>What’s on for me </li></ul><ul><li>Related Programs </li></ul>http://ifanzy.nl
  40. 42. NoTube Demonstrator III: Social TV & Web <ul><li> http://vimeo.com/10553773 </li></ul><ul><li> http://vimeo.com/11232681 </li></ul>
  41. 43. Acknowledgements & Image Credits <ul><li>Libby Miller, BBC </li></ul><ul><li>Vicky Buser, BBC </li></ul><ul><li>Dan Brickley, VUA </li></ul><ul><li>Guus Schreiber, VUA </li></ul><ul><li>Natalia Stash, TUe </li></ul><ul><li>Yiwen Wang, TUe </li></ul><ul><li>Peter Gorgels, RMA </li></ul><ul><li>http://pidgintech.com </li></ul><ul><li>Stoneroos </li></ul><ul><li>RAI </li></ul>
  42. 44. IMAGE CREDITS <ul><li>http://pidgintech.com </li></ul><ul><li>Dan Brickley, VU Amsterdam </li></ul><ul><li>Libby Miller, BBC </li></ul><ul><li>Vicky Buser, BBC </li></ul><ul><li>Stoneroos </li></ul>

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