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Patterns for Personalization on the Web
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Patterns for Personalization on the Web

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Lora Aroyo, talk at http://www.ec.tuwien.ac.at/trends

Lora Aroyo, talk at http://www.ec.tuwien.ac.at/trends

Published in Technology , Education
  • Full Name Full Name Comment goes here.
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  • from the CHIP project user tests. Yes, interestingness was tested in user studies.
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  • Which vocabularies do you use for user profile representation, user modelling and recommendation representation?
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  • This figure is from a paper on the CHIP project, published at either K-CAP2009 or EKAW2010, but in any case it could be found back in Yiwen's thesis. We identified 'intrestingness' and 'serendipity'/surprisingly interesting as the two metrics for testing the recommendations with semantics (as just pure precision and recall didn't work for us). All the data for this comes from the user studies.

    Actually, I am interested in performing similar experiments with the data in NoTube, but with one difference, also to have a learning (machine learning) included. What do you think?
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  • Lora, where's the figure in slide 17 from? Is 'interestingness' from user studies?
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  • Next to semantics we also deal with user data
  • 3 more examples of interactive apps taking user profiles and context into account
  • 3 more examples of interactive apps taking user profiles and context into account
  • 3 more examples of interactive apps taking user profiles and context into account
  • 3 more examples of interactive apps taking user profiles and context into account
  • 3 more examples of interactive apps taking user profiles and context into account
  • Interesting related problem Relatioships
  • 3 more examples of interactive apps taking user profiles and context into account
  • 3 more examples of interactive apps taking user profiles and context into account
  • 3 more examples of interactive apps taking user profiles and context into account
  • One step back If in general we do something with users and semantics Have to overlay the users with semantics - contextualizing (context can be very difficult – place, time activity) Granularity of data - number of types of formats - multiple applcations
  • 3 more examples of interactive apps taking user profiles and context into account
  • Delicious-> social bookmarking Last.fm->music Identi.ca-> microblogging FOAF Social Graph QDOS-> profile on the web Oauth: securi API

Transcript

  • 1. 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
  • 2. 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
  • 3. Application  Domains     @  VU  Amsterdam  
  • 4. 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
  • 5. 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
  • 6. enriched  rijksmuseum  collection  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 7. 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
  • 8. semantic  recommendations  
  • 9. semantic  artwork  presentation  
  • 10. semantic  explanations  
  • 11. how  did  we  start  …  WordNet  patterns  for  query   expansion  
  • 12. patterns  of  semantic  relations  in   WordNet  •  Hollink,  et.  Al  (2007)  
  • 13. 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
  • 14. 11  semantic  relationships  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 15. 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
  • 16. 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
  • 17. 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
  • 18. what  to  watch  tonight?   Personalized  Program  Guide  with   Social  Web  Activities   http://notube.tv    http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 19. deciding  what  to  watch  is  difficult  http://www.cs.vu.nl/~laroyo
  • 20. Can  Linked  Data  Help?  can  linked  open  data  help?  
  • 21. first  we  …  •  select  media-­‐related  Linked  Data  •  semantically  enrich  TV  program  metadata  •  define  similarity  measures  for  TV  programs  •  semantic  content-­‐based  recommendations  
  • 22. 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
  • 23. enrichment  of  TV  metadata  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 24. 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
  • 25. 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    
  • 26. 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
  • 27. 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
  • 28. 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
  • 29. 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
  • 30. 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
  • 31. where  do  we  use  all  this  …   for  recommendations  of  content  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 32. 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
  • 33. finding  interesting  relations   •  deep  links     •  related  info   •  granularity  of  content     – for  discussion   – for  user  feedback  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 34. distributed  context  http://www.cs.vu.nl/~laroyo © danbri
  • 35. cross-­‐domain  recommendations   •  domain   independent   content   patterns   •  context  (in-­‐) dependency   •  cross-­‐ application   •  cross-­‐domain  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 36. 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
  • 37. relevance  to  the  user?  http://www.cs.vu.nl/~laroyo © danbri
  • 38. 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  
  • 39. 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
  • 40. user  profiling  -­‐  activity  streams  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 41. NoTube  BeanCounter:   aggregating  &  profiling  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 42. 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
  • 43. http://notube.tv NOTUBE  DEMONSTRATORS   • http://vimeo.com/10553773 • http://vimeo.com/11232681© Libby Miller, BBC
  • 44. NoTube  Demonstrator  I:   Personalized  Semantic  News  http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 45. 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
  • 46. NoTube  Demonstrator  III:     Social  TV  &  Web   • http://vimeo.com/10553773 • http://vimeo.com/11232681http://www.cs.vu.nl/~laroyo twitter: @laroyo
  • 47. 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