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Aggregated, Interoperable and Multi-Domain User Profiles for the Social Web

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

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

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  • 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. Enabling Networked Knowledge
  • 2. User Profiling on the Social WebDigital Enterprise Research Institute www.deri.ie Disconnected social websites Isolated data silos http://www.w3.org Enabling Networked Knowledge
  • 3. Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge
  • 4. Our SolutionDigital Enterprise Research Institute www.deri.ie Interlink social websites Integration & Merge and model user data User Modelling User Profile Personalise users’ experience using their profile Recommendations Adaptive Systems Search Personalisation Enabling Networked Knowledge
  • 5. Linking Open DataDigital Enterprise Research Institute www.deri.ie  The Web of Data: a continuously evolving “open corpus” LOD Cloud by R. Cyganiak5 and A. Jentzsch Enabling Networked Knowledge
  • 6. Representing User Profiles of InterestDigital Enterprise Research Institute www.deri.ie dbp: Semantic_Web foaf:topic_interest wi:topic 0.7foaf: 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 Enabling Networked Knowledge 6
  • 7. Software architectureDigital Enterprise Research Institute www.deri.ie7 Enabling Networked Knowledge
  • 8. Service-specific Data CollectorDigital Enterprise Research Institute www.deri.ie  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 Enabling Networked Knowledge 8
  • 9. Data Analyser & Profile GeneratorDigital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 9
  • 10. Data Analyser & Profile GeneratorDigital Enterprise Research Institute www.deri.ie  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 Enabling Networked Knowledge 10
  • 11. Profiles AggregatorDigital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 11
  • 12. Profiles AggregatorDigital Enterprise Research Institute www.deri.ie  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 Enabling Networked Knowledge 12
  • 13. DBpedia Resources vs. CategoriesDigital Enterprise Research Institute www.deri.ie  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 … … … … Enabling Networked Knowledge
  • 14. Categories weighting-schemesDigital Enterprise Research Institute www.deri.ie  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. Enabling Networked Knowledge
  • 15. ExperimentDigital Enterprise Research Institute www.deri.ie  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 Enabling Networked Knowledge
  • 16. ExperimentDigital Enterprise Research Institute www.deri.ie  6 types of user profiles evaluated: Res Cat Cat1 Cat2 Res-120 Res-360 Cat1-120 Cat1-360 Cat2-120 Cat2-360 Enabling Networked Knowledge
  • 17. User-based EvaluationDigital Enterprise Research Institute www.deri.ie  21 users:  21 to 45 years old – 76% IT students/researchers  Average User Activity: Enabling Networked Knowledge 17
  • 18. User-based EvaluationDigital Enterprise Research Institute www.deri.ie  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 dont 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” Enabling Networked Knowledge 18
  • 19. Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge
  • 20. Categories vs. ResourcesDigital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge
  • 21. Cat1 vs. Cat2 (Cat.Discount)Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge
  • 22. t120 vs. t360Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge
  • 23. EvaluationDigital Enterprise Research Institute www.deri.ie  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 Enabling Networked Knowledge
  • 24. ConclusionsDigital 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) Enabling Networked Knowledge
  • 25. ThanksDigital Enterprise Research Institute www.deri.ie Contacts: Fabrizio Orlandi http://bit.ly/orlandi fabrizio.orlandi@deri.org @BadmotorF Enabling Networked Knowledge