Aastha jain youtube 05 sept 2012.pptx

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  • 1. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 2. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 3. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 4. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 5. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 6. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 7. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 8. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 9. Multi domain recommendation systemMOVIES BOOKS MUSIC
  • 10. User characterization by item preferences Movies BooksJohn likes:Mary likes: Recommendations:Tom likes: ??
  • 11. User characterization by item preferences Movies BooksJohn likes:Mary likes: Recommendations (domain-specific): Popular books suggestedTom likes: since no prior information is available
  • 12. User characterization by item preferences Movies BooksJohn likes:Mary likes: Recommendations (multi-domain): Based on Tom’s movie preferences, and otherTom likes: users’ cross-domain information
  • 13. User characterization by item preferences Movies BooksJohn likes:Mary likes:Tom likes:
  • 14. User characterization by item preferences Movies BooksJohn likes:Mary likes: Recommendations (multi-domain):Tom likes:
  • 15. User characterization by item preferences Movies BooksJohn likes:Mary likes: Recommendations (multi-domain):Tom likes:
  • 16. New items or sparsely rated items • New movie • Very few user ratings • Cannot be correctly classified and recommended • Use meta information • Easier to identify similar items
  • 17. New items or sparsely rated itemsTom likes:
  • 18. New items or sparsely rated itemsTom likes: Recommendations: OOPS!
  • 19. New items or sparsely rated itemsTom likes: Recommendations: OK!
  • 20. # items rated by user Average squared error User Domain Specific Recommendation# items rated by user Average squared error User Multi Domain Recommendation
  • 21. Domain Specific Recommendation Multi Domain RecommendationAverage squared error Average squared error 62.56% decrease User in root mean User squared error# items rated by user # items rated by user
  • 22. Quantitative Improvement Method Percentage Books Music Video accuracy (220,000 (80,000 (22,000 ratings) ratings) ratings)Collaborative 49.4 50.21 48.1 47.7 Filtering (domain specific) Collaborative 63.2 65.67 60.22 57.34 Filtering(multi-domain) Hybrid 76.4 77.13 75.11 73.87(Multi-domainCollaborative +content based)
  • 23. Aastha Jain