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Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
Aastha jain youtube 05 sept 2012.pptx
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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

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