WP	  3                             User	  profiling	  and	                            Recommenda5on	  (Part	  1)           ...
Contents         Overview         User profiling                 General goal & approach                 From activity str...
Overview                                              Semantic Content                   Semantic                         ...
Overview                                               Semantic Content                   Semantic                        ...
User profiling approach        users’ interests and behaviours could be inferred from        their activities on the Socia...
User profiling: Challenge        main challenge: extracting meaningful data from        different sources of user activiti...
User profiling: Follow-your-nose        “follow-your-nose”, record-linkage based            record linkage is “the problem...
User profiling: Semantic                                Annotation        for some activities the “follow-your-noise” appr...
User profiling: Semantic                                Annotation                26-27 March 2012      NoTube 3rd Review ...
User profiling: Semantic                                Annotation                  Bubbles Devere is the best thing ever....
User profiling: Semantic                                Annotation                  Bubbles Devere is the best thing ever....
User profiling: Semantic                                Annotation                  Bubbles Devere is the best thing ever....
User profiling: Issues         non-deterministic record-linkage and semantic annotation         could introduce noise     ...
Analytics        “people are usually interested in information about themselves”                                          ...
NoTube Beancounter        The User profiling and analytics components has been        lovingly called “Beancounter” since ...
NoTube Beancounter                                       key     value                      analysis     {                ...
Acknowledgements                26-27 March 2012   NoTube 3rd Review   13Wednesday, March 28, 12
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NoTube: User Profiling (Beancounter)

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NoTube: User Profiling (Beancounter)

  1. 1. WP  3 User  profiling  and   Recommenda5on  (Part  1) BBC,  Pro-­‐ne+cs,  VUA 1Wednesday, March 28, 12
  2. 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. 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. 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. 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. 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. 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. 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. 9. User profiling: Semantic Annotation 26-27 March 2012 NoTube 3rd Review 8Wednesday, March 28, 12
  10. 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. 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. 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. 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. 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. 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. 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. 17. Acknowledgements 26-27 March 2012 NoTube 3rd Review 13Wednesday, March 28, 12

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