1. WP
3
User
profiling
and
Recommenda5on
(Part
1)
BBC,
Pro-­‐ne+cs,
VUA
1
Wednesday, March 28, 12

2. Contents
Overview
User profiling
General goal & approach
From activity streams to profile
Issues
Analytics
Beancounter
Recommendations
General goal & approach
Semantic recommendation
Statistical recommendation
Hybrid recommendation
Exploitation
Conclusions
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3. Overview
Semantic Content Semantic
Patterns for Pattern-based
TV Programs Recommendation
EPG Metadata TV Program
Strategy
(BBC) Enrichment
RDF Graph Statistical
TV Recommendation Similarity-based
Programs Service Recommendation
Strategy
User Ratings &
Demographics User Data Similarity
(BBC EPG Analysis Clusters Hybrid
Data) of Programs Recommendation
Strategy
End End-Users
Users
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Wednesday, March 28, 12

4. Overview
Semantic Content Semantic
Patterns for Pattern-based
TV Programs Recommendation
EPG Metadata TV Program
Strategy
(BBC) Enrichment
RDF Graph Statistical
TV Recommendation Similarity-based
Programs Service Recommendation
Strategy
User Ratings &
Demographics User Data Similarity
(BBC EPG Analysis Clusters Hybrid
Data) of Programs Recommendation
Strategy
BEA
NCO
UNT
E R
End End-Users
Users
26-27 March 2012 NoTube 3rd Review 3
Wednesday, March 28, 12

5. User profiling approach
users’ interests and behaviours could be inferred from
their activities on the Social Web
• from tweets,
• liked facebook resources,
• song listened
• ...
interests in topics are represented using Linked Data web
identifiers
• to access a wealth of open and machine-readable data
• to publish profiles in compliance with the LOD paradigm
• to leverage on the graph-based model of such data sets
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6. User profiling: Challenge
main challenge: extracting meaningful data from
different sources of user activities
to produce LOD identifiers from activities:
• “follow-your-nose”, record-linkage based approach
• semantic-annotation-based approach, NLP techniques on raw text
interests are weighted to represent their descriptiveness
user profiles are syndicated using JSON, JSON-P and RDF
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7. User profiling: Follow-your-nose
“follow-your-nose”, record-linkage based
record linkage is “the problem of recognising those records in
two files which represent identical persons, objects or events
(said to be matched).”
we adopted a text retrieval version, incremental constrained
multiple text searches
facebook.com/pages/Shoeshine/ dbpedia.org/resource/
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8. User profiling: Semantic
Annotation
for some activities the “follow-your-noise” approach is not
suitable
Tweet, or text resources need Natural Language Processing
techniques
• semantic annotation using LUpedia (WP4)
lookup for LOD identifiers from:
• tweet text
• #hashtags definitions
• linked Web pages
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9. User profiling: Semantic
Annotation
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10. User profiling: Semantic
Annotation
Bubbles Devere is the best thing ever.
#littlebritain
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11. User profiling: Semantic
Annotation
Bubbles Devere is the best thing ever.
#littlebritain
Brilliant british humor by Matt Lucas & David
Walliams - whole range of facinating characters
portraying diversity of british society
26-27 March 2012 NoTube 3rd Review 8
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12. User profiling: Semantic
Annotation
Bubbles Devere is the best thing ever.
#littlebritain
Brilliant british humor by Matt Lucas & David
Walliams - whole range of facinating characters
portraying diversity of british society
WP4
Enrichment
http://dbpedia.org/resource/Matt_Lucas
http://dbpedia.org/resource/David_Walliams
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13. User profiling: Issues
non-deterministic record-linkage and semantic annotation
could introduce noise
• noisy data leads to misleading profiles
• recommendations could be affected
hence, we introduced interest weights
• to minimise the effect of potential noise eliminating poorly descriptive
interests giving them lower weights
• to represent the evolution of a single interest
recurring interest over time gain more weights
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14. Analytics
“people are usually interested in information about themselves”
from Doppler annual report
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15. NoTube Beancounter
The User profiling and analytics components has been
lovingly called “Beancounter” since the early days
built on top of experience and experiments made during
the 3 years of the project
a scalable, activity-streams-oriented set of processes
• filtering, slicing, fast key lookups
• many analysis are really just “counting the beans”
• analysis deserves an high performance architecture
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16. NoTube Beancounter
key value
analysis {
crawler
activities
{
{
analysis profiler
profiles engine
REST platform
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17. Acknowledgements
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