Presentation given by Fabrizio Orlandi at I-Semantics 2012, Graz, Austria. More info at http://bit.ly/orlandi and http://i-semantics.tugraz.at/

1. Digital Enterprise Research Institute www.deri.ie
Aggregated, Interoperable and Multi-Domain
User Profiles for the Social Web
Fabrizio Orlandi, John G. Breslin, Alexandre Passant
I-Semantics – Graz, Austria – 5-7 Sept. 2012
Copyright 2011 Digital Enterprise Research Institute. All rights reserved.
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2. User Profiling on the Social Web
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Disconnected
social
websites
Isolated
data
silos
http://www.w3.org
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3. Digital Enterprise Research Institute www.deri.ie
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4. Our Solution
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Interlink social websites
Integration
& Merge and model user data
User Modelling
User Profile
Personalise users’ experience
using their profile
Recommendations Adaptive Systems
Search Personalisation
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5. Linking Open Data
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 The Web of Data: a continuously evolving “open corpus”
LOD Cloud by R. Cyganiak
5 and A. Jentzsch
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6. Representing User Profiles of Interest
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dbp: Semantic_Web
foaf:topic_interest
wi:topic
0.7
foaf: Person wo:weight_value
wi:preference
wo:weight
wi:Weighted_Interest wo:Weight
wo:scale
opm: wasDerivedFrom
1.0 wo:Scale
wo:max_weight
sioc:UserAccount
0.0 wo:min_weight
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7. Software architecture
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7
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8. Service-specific Data Collector
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 Facebook and Twitter sources
 OAuth 2.0 user authentication system
 PHP libraries: Facebook PHP-SDK, Twitter-async
 Data collected from APIs: (up to 1 year back)
– User messages, posts, comments
– Likes
– Check-in
– Profile information
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9. Data Analyser & Profile Generator
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10. Data Analyser & Profile Generator
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 Natural Language Processing tool: Zemanta
 Used to spot entities on the collected data and link to DBpedia
 List of entities as interests
 Named entities (DBpedia URIs), their occurrences and metadata (provenance)
are recorded.
 Interest Weighting Strategy
 Based on frequency and time distance.
– Frequency => counting the number of occurrences
– Time Distance => using Exponential Time Decay function
t/τ
x(t) x0 e mean lifetime
 RDF representation of interests and weights
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11. Profiles Aggregator
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12. Profiles Aggregator
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 Aggregation of the different platform-specific profiles in one global
user profile of interests
 Easy aggregation of the interests using RDF
 Triples merged in the triplestore
 Provenance of the interests preserved
 Aggregation of the weights
Gi Ws wis
Weight of i in s
s
Global weight
interest i Source s Weight of source s
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13. DBpedia Resources vs. Categories
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 A user profile as a ranked list of DBpedia Resources or Categories
Dbpedia Resources weight DBpedia Categories weight
The_Clash 0.82 Buzzwords 0.48
Alternative_rock 0.71 Semantic_Web 0.87
Semantic_Web 0.48 Web_Services 0.48
Social_media 0.42 World_Wide_Web 0.39
Linked_Data 0.39 Hypermedia 0.39
… … … …
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14. Categories weighting-schemes
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 1st Strategy (Cat1):
 Weights of the Resources/Interests propagated to the related
Categories
 Cat1 Weight = Sum of the weights of the Category’s
Resources
 2nd Strategy (Cat2):
 Same as 1st Strategy but with discount for “broad” Categories
1 1
Cat Discount
log ( SP ) log ( SC )
where: SP = Set of Pages belonging to the Category,
SC = Set of Sub-Categories.
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15. Experiment
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 6 types of user profiles evaluated:
 2 types of DBpedia entities
– Categories vs. Resources
 2 types of weighting-scheme for category-based methods
– Cat1: Interests Weight Propagation
– Cat2: Interests Weight Propagation w/ Cat. Discount
 2 types of exponential Time Decay function
– Short mean lifetime 120 days
– Long mean lifetime 360 days
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16. Experiment
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 6 types of user profiles evaluated:
Res Cat
Cat1 Cat2
Res-120 Res-360 Cat1-120 Cat1-360 Cat2-120 Cat2-360
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17. User-based Evaluation
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 21 users:
 21 to 45 years old – 76% IT students/researchers
 Average User Activity:
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18. User-based Evaluation
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 We asked users to rate the top 10 interests generated for each of
the 6 profiling strategies
 Question:
“Please rate how relevant is each concept for representing your
personal interests and context…”
 Rating:
0 (not at all or don't know), 1 (low), 2, 3, 4, 5 (high)
 Rating converted to a (0…10) scale
 Performance evaluated with:
 MRR (Mean Reciprocal Rank)
 P@10 (Precision at K = 10)
 Comparison with a Baseline
 A traditional approach based on “keyword frequency”
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19. Digital Enterprise Research Institute www.deri.ie
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20. Categories vs. Resources
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21. Cat1 vs. Cat2 (Cat.Discount)
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22. t120 vs. t360
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23. Evaluation
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 On average for:
 200 Tweets
 200 Facebook posts, and items.
~106 interests - DBpedia Resources
~720 interests – DBpedia Categories (~7 times)
 Statistical significance (t-Test & Wilcoxon’s test) for:
 Resources vs. Categories (p<0.05)
 Any method vs. Baseline (p<0.05)
 Not for time decay (p~0.2) and Cat1 vs. Cat2
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24. Conclusions
Digital Enterprise Research Institute www.deri.ie
 User profiles generated with DBpedia Resources are more accurate than
with Categories.
 Using Categories generates 7 times more entities than using Resources
(and comparable accuracy)
 Useful for Recommendation Systems.
 Semantics + disambiguation + time decay function outperforms traditional
keyword-based methods.
 Insight:
 Sometimes Resources “too specific” and Categories “too broad”:
=> Mixed approach to be explored.
 TODO: Evaluation in different scenarios (e.g. Recommendations)
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25. Thanks
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Contacts:
Fabrizio Orlandi
http://bit.ly/orlandi
fabrizio.orlandi@deri.org
@BadmotorF
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