Mining Big Data and OpenKnowledge Sources to developtransparent and serendipitouscontent-based adaptive systemsCataldo Mus...
state of the art.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitous...
our research: personalizationC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and s...
Recommender SystemsRelevant items (movies, news, books, etc.) are pushed to theuser according to her preferences or her ne...
Amazon.comRecommendationsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and seren...
current recommendation technologies share threeimportant drawbacks.C.Musto, G.Semeraro - Mining Big Data and Open Knowledg...
(1) training is a bottleneck.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and s...
need forexplicitinformationaboutuser interests.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop...
(2) recsys are black boxes.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and ser...
(3) suggestions are not surprising.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent...
exploiting big data to build a novel generationof content-based adaptive systemssolutionC.Musto, G.Semeraro - Mining Big D...
current work.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscont...
C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based ada...
big data.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-...
InformationOverloadwe can handle 126 bits of informationwe deal with 393 bits of informationratio: more than 3x(Source: Ad...
Information OverloadC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipit...
Big Data: obstacle oropportunity?C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent a...
cornestone 1C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitousconte...
social media are an opportunityprovide information about user preferencesC.Musto, G.Semeraro - Mining Big Data and Open Kn...
exampleuser preferences in music from FacebookC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop ...
implicit preferencesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipit...
Play.meplaylistMost popular songs of the artists extracted from Last.fm (as well asthose added through the enrichment) are...
MyusicrecommendationsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipi...
cornestone 2exploit entity linking algorithmsto make user profiles moretransparent and LOD-awareC.Musto, G.Semeraro - Minin...
MyFeedsRSS recommendationsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and sere...
MyFeedstransparent user preferencesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent...
MyFeedstransparent user preferencesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent...
MyFeedsentity linking algorithmsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent an...
Tag.meextracts the Wikipedia pages the content refers to.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources ...
Linked Open Data CloudStructured(RDF)representationof the informationstored in Wikipedia.C.Musto, G.Semeraro - Mining Big ...
Linked Open Data CloudProfiles basedon Tag.me areLOD-awareC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources ...
cornestone 3C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitousconte...
‘in vitro’ experimentsWatchmi plug-indeveloped by Aprico.tvC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Source...
From BOW to eBOWGiven a description of a TV show, we exploit ESA toobtain an enhanced representationThe original set of fe...
TV SHOWRad an RadDie besten Duelle der MotoGP(Wheel to wheelThe best duels in the MotoGP)Wikipedia(Articles(großer&preis&v...
challenges.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitousconten...
Challenges and IssuesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipi...
RecommendationsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitousco...
questions?Cataldo Musto, Ph.D. - cataldo.musto@uniba.it
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Mining Big Data and Open Knowledge Sources to develop transparent and serendipitous content-based adaptive systems

  1. 1. Mining Big Data and OpenKnowledge Sources to developtransparent and serendipitouscontent-based adaptive systemsCataldo Musto, Giovanni Semeraro, Fedelucio Narducci
  2. 2. state of the art.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  3. 3. our research: personalizationC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  4. 4. Recommender SystemsRelevant items (movies, news, books, etc.) are pushed to theuser according to her preferences or her needs.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  5. 5. Amazon.comRecommendationsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  6. 6. current recommendation technologies share threeimportant drawbacks.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  7. 7. (1) training is a bottleneck.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  8. 8. need forexplicitinformationaboutuser interests.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  9. 9. (2) recsys are black boxes.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  10. 10. (3) suggestions are not surprising.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  11. 11. exploiting big data to build a novel generationof content-based adaptive systemssolutionC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  12. 12. current work.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013near future work.
  13. 13. C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  14. 14. big data.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  15. 15. InformationOverloadwe can handle 126 bits of informationwe deal with 393 bits of informationratio: more than 3x(Source: Adrian C.Ott,The 24-hour customer)consequence:C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  16. 16. Information OverloadC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  17. 17. Big Data: obstacle oropportunity?C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  18. 18. cornestone 1C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013exploit social media tomodel userpreferences.
  19. 19. social media are an opportunityprovide information about user preferencesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  20. 20. exampleuser preferences in music from FacebookC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  21. 21. implicit preferencesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013example
  22. 22. Play.meplaylistMost popular songs of the artists extracted from Last.fm (as well asthose added through the enrichment) are proposed to the user.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  23. 23. MyusicrecommendationsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  24. 24. cornestone 2exploit entity linking algorithmsto make user profiles moretransparent and LOD-awareC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  25. 25. MyFeedsRSS recommendationsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  26. 26. MyFeedstransparent user preferencesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013extracted from Facebook.
  27. 27. MyFeedstransparent user preferencesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013further processing
  28. 28. MyFeedsentity linking algorithmsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013• They map free text with structuredinformation• Wikipedia pages or DBpedia nodes• examples• Tag.me ,Wikipedia Miner, DBpediaSpotlight, etc.
  29. 29. Tag.meextracts the Wikipedia pages the content refers to.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  30. 30. Linked Open Data CloudStructured(RDF)representationof the informationstored in Wikipedia.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  31. 31. Linked Open Data CloudProfiles basedon Tag.me areLOD-awareC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  32. 32. cornestone 3C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013exploit open knowledge sourcesto make recommendationtechniques more serendipitous.
  33. 33. ‘in vitro’ experimentsWatchmi plug-indeveloped by Aprico.tvC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  34. 34. From BOW to eBOWGiven a description of a TV show, we exploit ESA toobtain an enhanced representationThe original set of features is enriched with the set ofWikipedia articles related the most with theTV showC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  35. 35. TV SHOWRad an RadDie besten Duelle der MotoGP(Wheel to wheelThe best duels in the MotoGP)Wikipedia(Articles(großer&preis&von&italien&(motorrad)&großer&preis&von&malaysia&(motorrad)&großer&preis&von&tschechien&(motorrad)&scuderia&ferrari&valen8no&rossi&motorrad9wm9saison&2005&motorrad9wm9saison&2006&max&biaggi&großer&preis&der&usa&(motorrad)&motorrad9wm9saison&2008&rad&(heraldik)&loris&capirossi&shin’ya&nakano&motogp&exampleFrom BOW to eBOWC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013
  36. 36. challenges.C.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013issues.recommendations.
  37. 37. Challenges and IssuesC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013• Main challenge and issue:• data representation and data filtering• How to exploit these novel data sylos?• What information is relevant for personalization?• What kind of processing do data need?• Which one is the best representation?• Do reasoning techniques improve profiles transparency andpersonalization accuracy?• Do people accept the exploitation of these data?• How to model the context?
  38. 38. RecommendationsC.Musto, G.Semeraro - Mining Big Data and Open Knowledge Sources to develop transparent and serendipitouscontent-based adaptive systems - World Summit on Big Data and Organization Design, Paris, 16-17 May 2013• Cornerstones• Social media-based user profiling• LOD-aware user profiles• Open Knowledge Sources for Serendipitous Encounters• Recommendations• Promote the LOD initiative, to publish data in a structuredform, to enable reasoning on the information• Make data sylos interconnected• To design applications able to properly model, manage andexploit the big amount of data coming from social media.
  39. 39. questions?Cataldo Musto, Ph.D. - cataldo.musto@uniba.it
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