Multi domain recommendation systemMOVIES           BOOKS               MUSIC
Multi domain recommendation systemMOVIES           BOOKS               MUSIC
Multi domain recommendation systemMOVIES           BOOKS               MUSIC
Multi domain recommendation systemMOVIES           BOOKS               MUSIC
Multi domain recommendation systemMOVIES           BOOKS               MUSIC
Multi domain recommendation systemMOVIES           BOOKS               MUSIC
Multi domain recommendation systemMOVIES           BOOKS               MUSIC
Multi domain recommendation systemMOVIES           BOOKS               MUSIC
Multi domain recommendation systemMOVIES           BOOKS               MUSIC
User characterization by item preferences               Movies                    BooksJohn likes:Mary likes:             ...
User characterization by item preferences               Movies                      BooksJohn likes:Mary likes:           ...
User characterization by item preferences               Movies                     BooksJohn likes:Mary likes:            ...
User characterization by item preferences               Movies                 BooksJohn likes:Mary likes:Tom likes:
User characterization by item preferences               Movies                 BooksJohn likes:Mary likes:                ...
User characterization by item preferences               Movies                 BooksJohn likes:Mary likes:                ...
New items or sparsely rated items                    • New movie                    • Very few user ratings               ...
New items or sparsely rated itemsTom likes:
New items or sparsely rated itemsTom likes:                   Recommendations:                                           O...
New items or sparsely rated itemsTom likes:                   Recommendations:                                            ...
# items rated by user          Average squared error                        User                                          ...
Domain Specific Recommendation                           Multi Domain RecommendationAverage squared error                 ...
Quantitative Improvement   Method         Percentage     Books      Music      Video                   accuracy    (220,00...
Aastha Jain
Upcoming SlideShare
Loading in...5
×

Aastha jain youtube 05 sept 2012.pptx

242

Published on

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
242
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Aastha jain youtube 05 sept 2012.pptx

  1. 1. Multi domain recommendation systemMOVIES BOOKS MUSIC
  2. 2. Multi domain recommendation systemMOVIES BOOKS MUSIC
  3. 3. Multi domain recommendation systemMOVIES BOOKS MUSIC
  4. 4. Multi domain recommendation systemMOVIES BOOKS MUSIC
  5. 5. Multi domain recommendation systemMOVIES BOOKS MUSIC
  6. 6. Multi domain recommendation systemMOVIES BOOKS MUSIC
  7. 7. Multi domain recommendation systemMOVIES BOOKS MUSIC
  8. 8. Multi domain recommendation systemMOVIES BOOKS MUSIC
  9. 9. Multi domain recommendation systemMOVIES BOOKS MUSIC
  10. 10. User characterization by item preferences Movies BooksJohn likes:Mary likes: Recommendations:Tom likes: ??
  11. 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. 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. 13. User characterization by item preferences Movies BooksJohn likes:Mary likes:Tom likes:
  14. 14. User characterization by item preferences Movies BooksJohn likes:Mary likes: Recommendations (multi-domain):Tom likes:
  15. 15. User characterization by item preferences Movies BooksJohn likes:Mary likes: Recommendations (multi-domain):Tom likes:
  16. 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. 17. New items or sparsely rated itemsTom likes:
  18. 18. New items or sparsely rated itemsTom likes: Recommendations: OOPS!
  19. 19. New items or sparsely rated itemsTom likes: Recommendations: OK!
  20. 20. # items rated by user Average squared error User Domain Specific Recommendation# items rated by user Average squared error User Multi Domain Recommendation
  21. 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. 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. 23. Aastha Jain
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×