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.

1. beancounter.io
a Social Web User Profiling as a Service
Davide Palmisano @dpalmisano
Wednesday, September 19, 2012, London

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. the Social Web
“the Social Web is currently used to describe
how people socialise or interact with
each other throughout the World Wide Web”

4. december 2007*
*from webarchive.org

5. today*
* http://www.readwriteweb.com/archives/alternate_reality_games_viral_marketing.php

7. semantic markup technologies and
authorisation protocols blurred the borders
between contents and users’ social graph

8. the Social Web is not only
aboutsocialising or
interacting with others

9. the Social Web is the place
users project their
where the
identity though consuming
contents

10. your app,
your
contents

11. your app,
your
contents

12. your app,
your
contents
engagement,
content syndication

13. your app,
your
contents
engagement,
content syndication
separated analytics,
content
recommendations

14. the illusion of content
personalisation
“are analytics the most you can get
from your audience?”

16. insights, analytics and statistics
are essentially
quantitative measures of
your audience
but there’s a lot more to be
discovered from your users

17. what are your users
interests?

18. what are their
preferences?

19. are there valuable
patterns between their
interest?

20. crunching the Social Web,
in real-time.
formerly known as Beancounter

21. each activity done on the Social
Web, carries some implicit
knowledge which could be
considered as a fraction of a
user’s identity

22. how we can make it explicit?
how we can represent it?
how to follow its evolution over
time?

23. anatomy of an activity
subject verb object context

24. anatomy of an activity
subject verb object context

25. anatomy of an activity
subject verb object context

26. anatomy of an activity
subject verb object context

27. anatomy of an activity
subject verb object context

28. every Web page text contains
entities potentially representative
of a user’ interest

29. Natural Language Processing
technologies are used to extract
named entities from textual
objects
and those named entities are
represented as Linked Open
Data identifiers.

30. Linked Data as Palette
picture by @danbri http://www.flickr.com/photos/danbri/3478830059/

31. http://dbpedia.org/page/Mario_Monti
http://dbpedia.org/page/Italy
http://dbpedia.org/page/Spain
http://dbpedia.org/page/
2007-2012_global_financial_crisis

33. named entities extraction,
text categorisation

34. named entities extraction,
text categorisation
record linkage

35. named entities extraction,
text categorisation
record linkage
old profile profile update

36. * for each incoming activity
named entities extraction,
text categorisation
record linkage
old profile profile update

38. record linkage

39. * owl:sameAs
record linkage
follow-your-nose
*

40. * owl:sameAs
record linkage
follow-your-nose
*
old profile profile update

41. * owl:sameAs
* for each incoming activity
record linkage
follow-your-nose
*
old profile profile update

42. Web identifiers
activities
profile weighting

43. your app,
your
contents

44. your app,
your
contents

45. your app,
your
contents

46. your app,
your
contents
engagement,
content
syndication

47. your app,
your
contents
engagement,
content
syndication
separated analytics,
content
recommendations

48. your app,
your
contents
engagement,
content
syndication
real-time
profiles
interest mining
(batch processes)
separated analytics,
content
recommendations

50. Now, think about having stored
all thesnapshots of your
users’ profiles in terms of
theirs weighted interests

51. interest mining, is that
process which allows you to
discover patterns and
relationships between di!erent
users’ interests

54. 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/“

55. curated
contents
TV broadcaster

56. curated
contents
TV broadcaster
login, comments,
sharing contents

57. curated
contents
TV broadcaster
login, comments,
real-time
sharing contents
profiles
interest mining
(batch processes)

58. curated
contents
TV broadcaster
TV archives
personal
recommendations
login, comments,
real-time
sharing contents
profiles
advertising,
audience tracking
interest mining
and identification
(batch processes)

59. 2nd screen iOS/android launch
foreseen for October 2012,
backed by beancounter.io
40K new users/week expected

61. a user watched something
from my archive
a user shared something on
Facebook

62. a user watched something
from my archive
generic interests layer
a user shared something on
Facebook

63. a user watched something
from my archive
custom profiling
rules
generic interests layer
a user shared something on
Facebook

64. a user watched something
from my archive
custom profiling
rules
application-specific
interests layer
generic interests layer
a user profile
a user shared something on
Facebook

65. a user watched something
from my archive
custom profiling
rules
application-specific
interests layer
generic interests layer
a user profile
a user shared something on
Facebook

66. 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

67. Davide Palmisano
@dpalmisano
http://launch.beancounter.io
crunching the Social Web, in real-time.