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Michele Minno, Balthasar Schopman, Libby Miller, Dan Brickley, Lora Aroyo

Profiling Users for TV by Using the Social Web

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Social Web: (isolated) Data Silos

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The User in Distributed Context

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NoTube Beancounter:Aggregating & Profiling

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From Activities to Interests

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Profiling the User in NoTube

•Collect user activities across the Social Web, e.g.

•likes, listen, watch, ...

•Model them with Atom Activity Streams in RDF Vocabulary (AAIR) http://xmlns.notu.be/aair

•Enrich them with DBpedia concepts

9the activity

the activity object

Dbpedia resource claimed to be sameAs

the song maker

RDF Graph of User Activities

From the Morning

Nick Drake

From the Morning

Play

http://dbpedia.org/resources/Nick_Drake

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•Resources related to user activities

•classified around SKOS concepts

User Profile Generation

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•Each DBpedia item can be classified by several DBPedia categories, e.g.

•Bill Gates:

•we select:•all•the best•the best set

• category:American_technology_writers• category:American_computer_programmers• category:Time_magazine_Persons_of_the_Year• category:Fellows_of_the_British_Computer_Society• category:Bill_Gates• category:Living_people• category:American_people_of_Scottish_descent• category:Harvard_University_people• category:1955_births• category:Windows_people• category:American_philanthropists• category:Microsoft_employees• category:National_Medal_of_Technology_recipients• category:People_from_King_County,_Washington• category:People_from_Seattle,_Washington• category:Bill_&_Melinda_Gates_Foundation_people• category:American_billionaires11

How we select?

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Representativeness of DBPedia Categories

•By popularity

• number of inbound object properties

•By co-occurrences

•among SKOS concepts

•using a mixed approach

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•Once computed the most representative DBPedia categories they are compared with metadata of the media item

•the DBPedia resource with the highest number of categories in common with our resource is the most similar to it

Selectingthe Best DBPedia

Resource

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Acknowledgements