NoTube: Pattern-based Recommendations (part 1)

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1 like•949 views

The document discusses recommendations for surfacing interesting, new, and relevant programs to individual and group users. It proposes combining statistical and semantic approaches in a complementary way. For the semantic recommendation approach, it involves analyzing linked open data sources to identify popular types and properties, and selecting relevant types and their patterns. User and program data would be enriched with concepts from knowledge bases before applying recommendation algorithms.

Technology Education

WP
3
User
proﬁling
and

Recommenda5on
(Part
2a)
BBC,
Pro-‐ne+cs,
VUA
1

Wednesday, March 28, 12

Recommendation

26-27 March 2012 NoTube 3rd Review 2

Wednesday, March 28, 12

Recommendation

surface interesting, new &
relevant programs to
individual and group users

26-27 March 2012 NoTube 3rd Review 2

Wednesday, March 28, 12

Recommendation

surface interesting, new &
relevant programs to
individual and group users

find the balance
between
serendipity
& diversity of
recommendations

26-27 March 2012 NoTube 3rd Review 2

Wednesday, March 28, 12

Recommendation

surface interesting, new &
relevant programs to
individual and group users

combine in a
find the balance
complementary way
between
statistical &
serendipity
semantic approaches
& diversity of
recommendations

26-27 March 2012 NoTube 3rd Review 2

Wednesday, March 28, 12

Recommendation

surface interesting, new &
relevant programs to
individual and group users

LOD

METADATA
combine in a
ENRICHMENT find the balance
complementary way
between
statistical & CONTENT serendipity
semantic approaches PATTERNS & diversity of
recommendations
DEMOGRAPHICS

26-27 March 2012 NoTube 3rd Review 2

Wednesday, March 28, 12

Recommendation

surface interesting, new &
relevant programs to
COLD individual and group users
START
LOD PRIVACY

METADATA
combine in a
ENRICHMENT find the balance
complementary way
between
statistical & CONTENT serendipity
semantic approaches PATTERNS & diversity of
recommendations
DEMOGRAPHICS

INTRUSIVENESS

26-27 March 2012 NoTube 3rd Review 2

Wednesday, March 28, 12

Semantic recommendation:
Data sources

• User data: Beancounter profiles
• weighted interests
• enriched with DBPedia concepts
• Program data: BBC TV metadata
• structured and textual description
• enriched with DBPedia, Freebase, LinkedMDB concepts
• Measures:
• popularity of metadata properties
• popularity of content patterns

26-27 March 2012 NoTube 3rd Review 3

Wednesday, March 28, 12

Semantic recommendation:
Process
1. Analysis of LOD sources

26-27 March 2012 NoTube 3rd Review 4

Wednesday, March 28, 12

Semantic recommendation:
Process
1. Analysis of LOD sources
Dataset LinkedMDB DBpedia Freebase
#triples 6,147,978 385,000,000 337,203,427
#props 221 1643 n.a.
#types 53 3,640,000 12,000,000

26-27 March 2012 NoTube 3rd Review 4

Wednesday, March 28, 12

Semantic recommendation:
Process
1. Analysis of LOD sources
2. Selection of popular and relevant types and their patterns

26-27 March 2012 NoTube 3rd Review 5

Wednesday, March 28, 12

Semantic recommendation:
Process
1. Analysis of LOD sources

2. Selection of popular and relevant types and their patterns
foaf:made
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movie:film foaf:Person
movie:director

od movie:actor
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:fil

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movie:performance
movie:film_crew_gig
(Example from LinkedMDB)

26-27 March 2012 NoTube 3rd Review 5

Wednesday, March 28, 12

Acknowledgements

26-27 March 2012 NoTube 3rd Review 6

Wednesday, March 28, 12

The document discusses user profiling and recommendation approaches. It describes extracting user interests from social media activities, representing interests as linked data identifiers, and addressing challenges like noise from record linkage. It also discusses generating recommendations using semantic, statistical, and hybrid approaches by analyzing user profiles and program metadata. Analytics on user profiles and activities are performed by the "Beancounter" system to support recommendations.

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