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.

1. WP
3
User
profiling
and
Recommenda5on
(Part
2a)
BBC,
Pro-­‐ne+cs,
VUA
1
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2. Recommendation
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3. Recommendation
surface interesting, new &
relevant programs to
individual and group users
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4. Recommendation
surface interesting, new &
relevant programs to
individual and group users
find the balance
between
serendipity
& diversity of
recommendations
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5. 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
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6. 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
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7. 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
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8. 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
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9. Semantic recommendation:
Process
1. Analysis of LOD sources
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10. Semantic recommendation:
Process
1. Analysis of LOD sources
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11. 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
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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
Properties #triples
foaf:page 512,944
http://purl.org/dc/terms/date 74,111
foaf:made 53,180
linkedmdb:actor_actorid 50,603
dbprop:hasPhotoCollection 30,354
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13. 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
Properties #triples
foaf:page 512,944
http://purl.org/dc/terms/date 74,111
foaf:made 53,180
linkedmdb:actor_actorid 50,603
dbprop:hasPhotoCollection 30,354
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14. Semantic recommendation:
Process
1. Analysis of LOD sources
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15. Semantic recommendation:
Process
1. Analysis of LOD sources
2. Selection of popular and relevant types and their patterns
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16. Semantic recommendation:
Process
1. Analysis of LOD sources
2. Selection of popular and relevant types and their patterns
foaf:made
rdfs:literal
movie:film foaf:Person
movie:director
od movie:actor
dli
lm
nk
m
_fi
m
:lin
ov
ce
ov
k _s
ie
ie
an
ou
:fil
:p
rc
rm
e
m
er
e
rfo
_c
fo
rm
re
e
:p
an
w_
ie
ce
g
ov
oddlink:interlink
ig
m
_
film
movie:performance
movie:film_crew_gig
(Example from LinkedMDB)
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17. Semantic recommendation:
Process
1. Analysis of LOD sources
2. Selection of popular and relevant types and their patterns
foaf:made
rdfs:literal
movie:film foaf:Person
movie:director
od movie:actor
dli
lm
nk
m
m
_fi
m
m
:lin
ov
ce
ov
ov
k
v
_s
iie
e:
iie
an
ou
e:
:p
f
fiillm
rc
pe
rm
e
er
e
rfo
rfo
_c
_c
fo
rm
rm
re
e
ew
:p
an
w_
ie
nc
_g
ce
g
ov
e
oddlink:interlink
iig
g_
m
_
fm
fiilm
Path of length 2
Frequency 3,851,200 movie:performance
movie:film_crew_gig
(Example from LinkedMDB)
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18. Semantic recommendation:
Process
1. Analysis of LOD sources
2. Selection of popular and relevant types and their patterns
foaf:made
rdfs:literal
movie:film foaf:Person
movie:director
od movie:actor
dli
lm
nk
m
_fi
m
:lin
ov
ce
ov
k _s
ie
ie
an
ou
:fil
:p
rc
rm
e
m
er
e
rfo
_c
fo
rm
re
e
:p
an
w_
ie
ce
g
ov
oddlink:interlink
ig
m
_
film
Path of length 2
Frequency 199,443 movie:performance
movie:film_crew_gig
(Example from LinkedMDB)
26-27 March 2012 NoTube 3rd Review 5
Wednesday, March 28, 12

19. Semantic recommendation:
Process
1. Analysis of LOD sources
2. Selection of popular and relevant types and their patterns
foaf:made
rdfs:literal
movie:film foaf:Person
movie:director
od movie:actor
dli
lm
nk
m
_fi
m
:lin
ov
ce
ov
k _s
ie
ie
an
ou
:fil
:p
rc
rm
e
m
er
e
rfo
_c
fo
rm
re
e
:p
an
w_
ie
ce
g
ov
oddlink:interlink
ig
m
_
film
Path of length 3
Frequency 6,032,799 movie:performance
movie:film_crew_gig
(Example from LinkedMDB)
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20. Semantic recommendation:
Process
1. Analysis of LOD sources
2. Selection of popular and relevant types and their patterns
foaf:made
rdfs:literal
movie:film foaf:Person
movie:director
od movie:actor
dli
lm
nk
m
_fi
m
:lin
ov
ce
ov
k _s
ie
ie
an
ou
:fil
:p
rc
rm
e
m
er
e
rfo
_c
fo
rm
re
e
:p
an
w_
ie
ce
g
ov
oddlink:interlink
ig
m
_
film
Path of length 3
Frequency 3,851,200 movie:performance
movie:film_crew_gig
(Example from LinkedMDB)
26-27 March 2012 NoTube 3rd Review 5
Wednesday, March 28, 12

21. Semantic recommendation:
Process
1. Analysis of LOD sources
2. Selection of popular and relevant types and their patterns
foaf:made
rdfs:literal
movie:film foaf:Person
movie:director
od movie:actor
dli
lm
nk
m
_fi
m
:lin
ov
ce
ov
k _s
ie
ie
an
ou
:fil
:p
rc
rm
e
m
er
e
rfo
_c
fo
rm
re
e
:p
an
w_
ie
ce
g
ov
oddlink:interlink
ig
m
_
film
Path of length 3
Frequency 3,409,791 movie:performance
movie:film_crew_gig
(Example from LinkedMDB)
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22. Acknowledgements
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