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Radio recommender system for FMHost

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Radio recommender system

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Radio recommender system for FMHost

  1. 1. Online Recommender Systemfor Radio Station HostingVasily Zaharchuk (HSE) Andrey Konstantinov (HSE)Dmitry Ignatov (HSE) Sergey Nikolenko (SMI RAS) BIR 2012 HSE, Nizhniy Novgorod
  2. 2. Outline• FMhost Online Radio Hosting• Recommender Model ▫ Data ▫ Model and Algorithms• Quality of Service Evaluation (QoS) ▫ User and Radio Station Activity Analaysis ▫ Evaluation Technique• Conclusion
  3. 3. Online Radio Hosting FMhost• FMhost.me or Host.fm• Real radio, not a streamer• Social network• Lives• New features• Listener oriented• Likes• Favorites
  4. 4. Users• Unauthorized• Listeners• DJs• Station owners
  5. 5. Recommender System Why is it needed?
  6. 6. The Previous Algorithm• Ignatov et al. 2011
  7. 7. Math• Math math math math math math math math• Math math math math math math math math• Math math math math math math math math• Math math math math math math math math• Math math math math math math math math• Math math math math math math math math• Math math math math math math math math
  8. 8. The Model: Data• U is a set of users, R is a set of radio stations, T is a set of tags• A=(aut), B=(brt), and C=(cur)• frequency vectors• Normalized matrices, e.g.
  9. 9. The Model
  10. 10. The Model: Importance Weights• Edwards, W. & Barron, F. (1994)
  11. 11. Individual-Based RS (Algorithm RecBi3.1)• RecBi3.1 uses Af and Bf• For a particular user• Rank function ,
  12. 12. Collaborative-Based RS (Algorithm RecBi3.2)• RecBi3.2 uses Cf and vector nC, the latter contains a total number of listened stations for each• D is a distance matrix• Top-k neighbors
  13. 13. Collaborative-Based RS (Algorithm RecBi3.2)• The set of listened stations• Top-N recommendations
  14. 14. End Recommender System (RecBi3.3)• Final ranking•
  15. 15. QoS: Distribution Analsysis• Looking for Power Law P(x)=Cx-
  16. 16. QoS: Distribution Analysis
  17. 17. QoS: Distribution Analysis• Pareto Principle (20%:80%)• 50%:80% for radio stations• 50%:83% for user visits
  18. 18. QoS assessment IBRS and CBRS
  19. 19. QoS assessment IBRS and CBRS ERS
  20. 20. Conclusion• Validation on new datasets• Scalability issues• Folksonomic nature of data & Triclustering
  21. 21. Q&A Thank you!

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