beancounter.ioa Social Web User Profiling as a ServiceDavide Palmisano @dpalmisanoWednesday, September 19, 2012, London
table of contents          the Social Web          the illusion of          content personalisation          beancounter.i...
the Social Web“the Social Web is currently used to describehow people socialise or interact witheach other throughout the ...
december 2007*    *from webarchive.org
today** http://www.readwriteweb.com/archives/alternate_reality_games_viral_marketing.php
semantic markup technologies andauthorisation protocols blurred the bordersbetween   contents and users’   social graph
the Social Web is not only aboutsocialising orinteracting with others
the Social Web is the place        users project theirwhere theidentity though consuming       contents
your app,  yourcontents
your app,  yourcontents
your app,          your        contents   engagement,content syndication
your app,             your           contents      engagement,   content syndicationseparated analytics,     content recom...
the illusion of content        personalisation      “are analytics the most you can get      from your audience?”
insights, analytics and statistics          are essentiallyquantitative measures of         your audiencebut there’s a lot...
what are your users   interests?
what are theirpreferences?
are there valuablepatterns between their       interest?
crunching the Social Web,             in real-time.formerly known as    Beancounter
each activity done on the Social Web, carries some   implicitknowledge which could be considered as a fraction of a     us...
how we can make it   explicit?   how we can   represent it?how to follow its evolution over             time?
anatomy of an activitysubject   verb   object   context
anatomy of an activitysubject   verb   object   context
anatomy of an activitysubject   verb   object   context
anatomy of an activitysubject   verb   object   context
anatomy of an activitysubject   verb   object   context
every Web page   text containsentities potentially representativeof a user’ interest
Natural Language Processingtechnologies are used to extract   named entities from textual             objects and those na...
Linked Data as Palette      picture by @danbri http://www.flickr.com/photos/danbri/3478830059/
http://dbpedia.org/page/Mario_Montihttp://dbpedia.org/page/Italyhttp://dbpedia.org/page/Spain  http://dbpedia.org/page/  2...
named entities extraction,   text categorisation
named entities extraction,   text categorisation     record linkage
named entities extraction,                text categorisation                  record linkageold profile         profile upd...
* for each incoming activity                               named entities extraction,                                  tex...
record linkage
*   owl:sameAs                   record linkage                 follow-your-nose                                    *
*   owl:sameAs                    record linkage                  follow-your-nose                                     *  ...
*   owl:sameAs     * for each incoming activity                                      record linkage                       ...
Web identifiersactivities             profile weighting
your app,  yourcontents
your app,  yourcontents
your app,  yourcontents
your app,                 your               contentsengagement,   content syndication
your app,                            your                          contents           engagement,              content    ...
your app,                                                your                                              contents       ...
Now, think about having storedall thesnapshots of yourusers’ profiles in terms of   theirs weighted interests
interest mining, is thatprocess which allows you to   discover patterns andrelationships between di!erent        users’ in...
a Social TV scenario     “60% of Americans use the      Web simultaneously while                  watching TV”http://blog....
curatedcontents           TV broadcaster
curated                   contents                              TV broadcasterlogin, comments,sharing contents
curated                   contents                                          TV broadcasterlogin, comments,                ...
curated                        contents                                                 TV broadcaster                    ...
2nd screen iOS/android launch  foreseen for October 2012,  backed by beancounter.io40K new users/week expected
a user watched something     from my archivea user shared something on         Facebook
a user watched something     from my archive                             generic interests layera user shared something on...
a user watched something     from my archive            custom profiling                 rules                             ...
a user watched something     from my archive            custom profiling                 rules                             ...
a user watched something     from my archive            custom profiling                 rules                             ...
beancounter.io in few words        Open Linked Data profiles, for interoperability     real-time computation, to closely fo...
Davide Palmisano                                        @dpalmisano   http://launch.beancounter.iocrunching the Social Web...
beancounter.io - Social Web user profiling as a service #semtechbiz
beancounter.io - Social Web user profiling as a service #semtechbiz
beancounter.io - Social Web user profiling as a service #semtechbiz
beancounter.io - Social Web user profiling as a service #semtechbiz
beancounter.io - Social Web user profiling as a service #semtechbiz
beancounter.io - Social Web user profiling as a service #semtechbiz
beancounter.io - Social Web user profiling as a service #semtechbiz
beancounter.io - Social Web user profiling as a service #semtechbiz
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beancounter.io - Social Web user profiling as a service #semtechbiz

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My slide deck from #semtechbiz 2012 in London about beancounter.io, a Web API platform to profile your users from the Social Web, in real-time.

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beancounter.io - Social Web user profiling as a service #semtechbiz

  1. 1. beancounter.ioa Social Web User Profiling as a ServiceDavide Palmisano @dpalmisanoWednesday, September 19, 2012, London
  2. 2. table of contents the Social Web the illusion of content personalisation beancounter.io: user profiling as a service a scenario for Social TV
  3. 3. the Social Web“the Social Web is currently used to describehow people socialise or interact witheach other throughout the World Wide Web”
  4. 4. december 2007* *from webarchive.org
  5. 5. today** http://www.readwriteweb.com/archives/alternate_reality_games_viral_marketing.php
  6. 6. semantic markup technologies andauthorisation protocols blurred the bordersbetween contents and users’ social graph
  7. 7. the Social Web is not only aboutsocialising orinteracting with others
  8. 8. the Social Web is the place users project theirwhere theidentity though consuming contents
  9. 9. your app, yourcontents
  10. 10. your app, yourcontents
  11. 11. your app, your contents engagement,content syndication
  12. 12. your app, your contents engagement, content syndicationseparated analytics, content recommendations
  13. 13. the illusion of content personalisation “are analytics the most you can get from your audience?”
  14. 14. insights, analytics and statistics are essentiallyquantitative measures of your audiencebut there’s a lot more to bediscovered from your users
  15. 15. what are your users interests?
  16. 16. what are theirpreferences?
  17. 17. are there valuablepatterns between their interest?
  18. 18. crunching the Social Web, in real-time.formerly known as Beancounter
  19. 19. each activity done on the Social Web, carries some implicitknowledge which could be considered as a fraction of a user’s identity
  20. 20. how we can make it explicit? how we can represent it?how to follow its evolution over time?
  21. 21. anatomy of an activitysubject verb object context
  22. 22. anatomy of an activitysubject verb object context
  23. 23. anatomy of an activitysubject verb object context
  24. 24. anatomy of an activitysubject verb object context
  25. 25. anatomy of an activitysubject verb object context
  26. 26. every Web page text containsentities potentially representativeof a user’ interest
  27. 27. Natural Language Processingtechnologies are used to extract named entities from textual objects and those named entities are represented as Linked Open Data identifiers.
  28. 28. Linked Data as Palette picture by @danbri http://www.flickr.com/photos/danbri/3478830059/
  29. 29. http://dbpedia.org/page/Mario_Montihttp://dbpedia.org/page/Italyhttp://dbpedia.org/page/Spain http://dbpedia.org/page/ 2007-2012_global_financial_crisis
  30. 30. named entities extraction, text categorisation
  31. 31. named entities extraction, text categorisation record linkage
  32. 32. named entities extraction, text categorisation record linkageold profile profile update
  33. 33. * for each incoming activity named entities extraction, text categorisation record linkage old profile profile update
  34. 34. record linkage
  35. 35. * owl:sameAs record linkage follow-your-nose *
  36. 36. * owl:sameAs record linkage follow-your-nose * old profile profile update
  37. 37. * owl:sameAs * for each incoming activity record linkage follow-your-nose * old profile profile update
  38. 38. Web identifiersactivities profile weighting
  39. 39. your app, yourcontents
  40. 40. your app, yourcontents
  41. 41. your app, yourcontents
  42. 42. your app, your contentsengagement, content syndication
  43. 43. your app, your contents engagement, content syndicationseparated analytics, content recommendations
  44. 44. your app, your contents engagement, content syndication real-time profiles interest mining (batch processes)separated analytics, content recommendations
  45. 45. Now, think about having storedall thesnapshots of yourusers’ profiles in terms of theirs weighted interests
  46. 46. interest mining, is thatprocess which allows you to discover patterns andrelationships between di!erent users’ interests
  47. 47. a Social TV scenario “60% of Americans use the Web simultaneously while watching TV”http://blog.nielsen.com/nielsenwire/online_mobile/three-screen-report-q409/“
  48. 48. curatedcontents TV broadcaster
  49. 49. curated contents TV broadcasterlogin, comments,sharing contents
  50. 50. curated contents TV broadcasterlogin, comments, real-timesharing contents profiles interest mining (batch processes)
  51. 51. curated contents TV broadcaster TV archives personal recommendationslogin, comments, real-timesharing contents profiles advertising, audience tracking interest mining and identification (batch processes)
  52. 52. 2nd screen iOS/android launch foreseen for October 2012, backed by beancounter.io40K new users/week expected
  53. 53. a user watched something from my archivea user shared something on Facebook
  54. 54. a user watched something from my archive generic interests layera user shared something on Facebook
  55. 55. a user watched something from my archive custom profiling rules generic interests layera user shared something on Facebook
  56. 56. a user watched something from my archive custom profiling rules application-specific interests layer generic interests layer a user profilea user shared something on Facebook
  57. 57. a user watched something from my archive custom profiling rules application-specific interests layer generic interests layer a user profilea user shared something on Facebook
  58. 58. beancounter.io in few words Open Linked Data profiles, for interoperability real-time computation, to closely follow your users fully customisable, to tail it on your domain available SaaS, in-house deployment baked by top-class open source products, lambda-architecture ** N. Marz, “Big Data”, Manning, 9781617290343
  59. 59. Davide Palmisano @dpalmisano http://launch.beancounter.iocrunching the Social Web, in real-time.
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