beancounter.io a Social Web User Profiling as a Service Davide Palmisano @dpalmisano Wednesday, September 19, 2012, London
beancounter.io - Social Web user profiling as a service #semtechbiz
Jan 15, 2015
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.ioa Social Web User Profiling as a Service
Davide Palmisano @dpalmisano Wednesday, September 19, 2012, London
table of contents
the Social Web
the illusion of content personalisation
beancounter.io: user profiling as a service
a scenario for Social TV
the Social Web
“the Social Web is currently used to describe
how people socialise or interact with each other throughout the World Wide Web”
december 2007**from webarchive.org
today** http://www.readwriteweb.com/archives/alternate_reality_games_viral_marketing.php
semantic markup technologies and authorisation protocols blurred the borders
between contents and users’ social graph
the Social Web is not only
about socialising or
interacting with others
the Social Web is the place
where the users project their
identity though consuming
contents
your app, your
contents
your app, your
contents
your app, your
contents
engagement,content syndication
your app, your
contents
separated analytics, content
recommendations
engagement,content syndication
the illusion of content personalisation
“are analytics the most you can get from your audience?”
insights, analytics and statistics are essentially
quantitative measures of your audience
but there’s a lot more to be
discovered from your users
what are your users
interests?
what are their
preferences?
are there valuable
patterns between their interest?
crunching the Social Web, in real-time.
Beancounterformerly known as
each activity done on the Social
Web, carries some implicit knowledge which could be
considered as a fraction of a
user’s identity
how we can make it explicit?
how we can represent it?
how to follow its evolution over time?
anatomy of an activity
subject verb object context
subject verb object context
anatomy of an activity
subject verb object context
anatomy of an activity
subject verb object context
anatomy of an activity
subject verb object context
anatomy of an activity
every Web page text contains
entities potentially representative
of a user’ interest
and those named entities are represented as Linked Open
Data identifiers.
Natural Language Processing technologies are used to extract
named entities from textual objects
Linked Data as Palette
picture by @danbri http://www.flickr.com/photos/danbri/3478830059/
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
named entities extraction, text categorisation
named entities extraction, text categorisation
record linkage
profile updateold profile
named entities extraction, text categorisation
record linkage
old profile
* for each incoming activity
profile update
named entities extraction, text categorisation
record linkage
record linkage
follow-your-nose
record linkage
*
*owl:sameAs
follow-your-nose
record linkage
profile updateold profile
*
*owl:sameAs
follow-your-nose
record linkage
profile updateold profile
* for each incoming activity
*owl:sameAs
*
activities
Web identifiers
profile weighting
your app, your
contents
your app, your
contents
your app, your
contents
your app, your
contents
engagement,content
syndication
your app, your
contents
separated analytics, content
recommendations
engagement,content
syndication
your app, your
contents
engagement,content
syndication
separated analytics, content
recommendations
real-time profiles
interest mining (batch processes)
Now, think about having stored
all the snapshots of your
users’ profiles in terms of theirs weighted interests
interest mining, is that process which allows you to
discover patterns and relationships between di!erent
users’ interests
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/“
TV broadcaster
curated contents
login, comments, sharing contents
TV broadcaster
curated contents
TV broadcaster
curated contents
login, comments, sharing contents
TV broadcaster
curated contents
real-time profiles
interest mining (batch processes)
TV broadcaster
curated contents
login, comments, sharing contents
TV broadcaster
curated contents
personal recommendations
real-time profiles
TV archives
interest mining (batch processes)
advertising, audience tracking and identification
40K new users/week expected
2nd screen iOS/android launch foreseen for October 2012, backed by beancounter.io
a user watched something from my archive
a user shared something on Facebook
a user watched something from my archive
a user shared something on Facebook
generic interests layer
a user watched something from my archive
a user shared something on Facebook
custom profiling rules
generic interests layer
a user watched something from my archive
a user shared something on Facebook
custom profiling rules
generic interests layer
application-specific interests layer
a user profile
a user watched something from my archive
a user shared something on Facebook
custom profiling rules
generic interests layer
application-specific interests layer
a user profile
beancounter.io in few words
Open Linked Data profiles, for interoperability
real-time computation, to closely follow your users
available SaaS, in-house deployment
fully customisable, to tail it on your domain
baked by top-class open source products, lambda-architecture
N. Marz, “Big Data”, Manning, 9781617290343*
*
crunching the Social Web, in real-time.
http://launch.beancounter.io
@dpalmisanoDavide Palmisano
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