Semantic	  Patterns	  for	  Web	                           Personalization       	                                        ...
the	  personalization	  challenge	       •  discover	  useful	  linked	  (open)	  data	  pa4erns	  	             – domain-...
Application	  Domains	  	   @	  VU	  Amsterdam	  
what’s	  interesting	  for	  me	  in	  the	                                   museum?     	                            Art...
museum	  metadata	  &	  vocabularies                                           	       •  Metadata	  format	  is	  Dublin-...
enriched	  rijksmuseum	  collection	  http://www.cs.vu.nl/~laroyo                   twitter: @laroyo
what	  can	  we	  do	  with	  semantics?                                                    	       •  Generate	  automa8c...
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,	  20...
11	  semantic	  relationships                                                    	  http://www.cs.vu.nl/~laroyo           ...
4	  artwork	  features                                                     	       •  link	  an	  artwork	  &	  its	  asso...
results	  …	                                                   •  vra:creator	  &	  link:hasStyle	                        ...
navigation	  patterns                                                       	                 • 	  	  artwork	  -­‐>	  cre...
what	  to	  watch	  tonight?                                                   	                        Personalized	  Pro...
deciding	  what	  to	  watch	  is	  difficult                                                     	  http://www.cs.vu.nl/~la...
Can	  Linked	  Data	  Help?   	  can	  linked	  open	  data	  help?  	  
first	  we	  …	  •  select	  media-­‐related	  Linked	  Data	  •  semantically	  enrich	  TV	  program	  metadata	  •  defin...
TV-­‐related	  linked	  data	       •  DBPedia,	  Freebase,	  WordNet(s)	       •  TV	  genre	  typologies,	  IMDB,	  TV	 ...
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	 ...
LOD	  is	  BIG	  &	  MESSY	                           many	  interesting	  facts          	     but	  also	  much	  straig...
source	  for	  noise	  in	  LOD	  …	       •  Multiple	  (large)	  vocabularies	  with	  various	          semantics	     ...
to	  filter	  out	  the	  noise	  in	  LOD	  …	                                we	  look	  for	                            ...
how	  did	  we	  do	  it	  …	       •  select	  the	  appropriate	  LOD	  sources	             – detect	  representative	 ...
method	       •  List	  of	  all	  Properties	  (P)	  as	  defined	  in	  their	          vocabulary	  (with	  domain	  and...
paths                                                   	       •  ordered	  list	  of	  properties	  from	  triple	  sequ...
where	  do	  we	  use	  all	  this	  …	                        for	  recommendations	  of	  content                       ...
recommendations	  with	  patterns                                          	       •  reduce	  the	  burden	  of	  too	  m...
finding	  interesting	  relations                                                 	       •  deep	  links	  	       •  rela...
distributed	  context                                                   	  http://www.cs.vu.nl/~laroyo                    ...
cross-­‐domain	  recommendations                                        	   •  domain	      independent	      content	    ...
generating	  explanations                                               	                                            •  He...
relevance	  to	  the	  user?	  http://www.cs.vu.nl/~laroyo                                     © danbri
next	  we	  …	  •  select	  only	  the	  LOD	  pa4erns	  that	  match	     relevance	  for	  a	  given	  user	  e.g.	  usi...
FOAF	  (Friend-­‐of-­‐a-­‐Friend)	                                     User	  Profile	  schema:	                        cap...
user	  profiling	  -­‐	  activity	  streams                                                        	  http://www.cs.vu.nl/~...
NoTube	  BeanCounter:	                          aggregating	  &	  profiling	  http://www.cs.vu.nl/~laroyo                  ...
patterns	  in	  social	  media	       •  Twitter	  TV	  trends	  in	  people	  I	  follow	             – what	  my	  frien...
http://notube.tv       NOTUBE	  DEMONSTRATORS	         • http://vimeo.com/10553773       • http://vimeo.com/11232681© Libb...
NoTube	  Demonstrator	  I:	                Personalized	  Semantic	  News	  http://www.cs.vu.nl/~laroyo                   ...
NoTube	  Demonstrator	  II:	                       Personalized	  EPG	  &	  Ads	          OnlineTV	  Guide	               ...
NoTube	  Demonstrator	  III:	                                                      	                         Social	  TV	 ...
Acknowledgements	  &	                                    Image	  Credits                                                 	...
Patterns for Personalization on the Web
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

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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
  • Patterns for Personalization on the Web

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

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