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  • 1. WP  3 User  profiling  and   Recommenda5on  (Part  1) BBC,  Pro-­‐ne+cs,  VUA 1Wednesday, 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 26-27 March 2012 NoTube 3rd Review 2Wednesday, March 28, 12
  • 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 26-27 March 2012 NoTube 3rd Review 3Wednesday, 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 3Wednesday, 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 26-27 March 2012 NoTube 3rd Review 4Wednesday, March 28, 12
  • 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 26-27 March 2012 NoTube 3rd Review 5Wednesday, March 28, 12
  • 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/ 26-27 March 2012 NoTube 3rd Review 6Wednesday, March 28, 12
  • 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 26-27 March 2012 NoTube 3rd Review 7Wednesday, March 28, 12
  • 9. User profiling: Semantic Annotation 26-27 March 2012 NoTube 3rd Review 8Wednesday, March 28, 12
  • 10. User profiling: Semantic Annotation Bubbles Devere is the best thing ever. #littlebritain 26-27 March 2012 NoTube 3rd Review 8Wednesday, March 28, 12
  • 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 8Wednesday, March 28, 12
  • 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 26-27 March 2012 NoTube 3rd Review 8Wednesday, March 28, 12
  • 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 26-27 March 2012 NoTube 3rd Review 9Wednesday, March 28, 12
  • 14. Analytics “people are usually interested in information about themselves” from Doppler annual report 26-27 March 2012 NoTube 3rd Review 10Wednesday, March 28, 12
  • 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 26-27 March 2012 NoTube 3rd Review 11Wednesday, March 28, 12
  • 16. NoTube Beancounter key value analysis { crawler activities { { analysis profiler profiles engine REST platform 26-27 March 2012 NoTube 3rd Review 12Wednesday, March 28, 12
  • 17. Acknowledgements 26-27 March 2012 NoTube 3rd Review 13Wednesday, March 28, 12