Aastha jain youtube 05 sept 2012.pptx

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Aastha Jain

Negócios Tecnologia

Multi domain recommendation system

MOVIES BOOKS MUSIC

User characterization by item preferences
Movies Books

John likes:

Mary likes:

Recommendations:

Tom likes:
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User characterization by item preferences
Movies Books

John likes:

Mary likes:

Recommendations (domain-specific):

Popular books suggested
Tom likes: since no prior information
is available

User characterization by item preferences
Movies Books

John likes:

Mary likes:

Recommendations (multi-domain):
Based on Tom’s movie
preferences, and other
Tom likes: users’ cross-domain
information

User characterization by item preferences
Movies Books

John likes:

Mary likes:

Tom likes:

User characterization by item preferences
Movies Books

John likes:

Mary likes:

Recommendations (multi-domain):

Tom likes:

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

New items or sparsely rated items
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# items rated by user Average squared error

User
Domain Specific Recommendation

# items rated by user Average squared error
User
Multi Domain Recommendation

Domain Specific Recommendation Multi Domain Recommendation
Average squared error

Average squared error
62.56% decrease
User
in root mean User

squared error
# items rated by user

# items rated by user

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
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7. Multi domain recommendation system MOVIES BOOKS MUSIC

8. Multi domain recommendation system MOVIES BOOKS MUSIC

9. Multi domain recommendation system MOVIES BOOKS MUSIC

10. User characterization by item preferences Movies Books John likes: Mary likes: Recommendations: Tom likes: ??

11. User characterization by item preferences Movies Books John likes: Mary likes: Recommendations (domain-specific): Popular books suggested Tom likes: since no prior information is available

12. User characterization by item preferences Movies Books John likes: Mary likes: Recommendations (multi-domain): Based on Tom’s movie preferences, and other Tom likes: users’ cross-domain information

13. User characterization by item preferences Movies Books John likes: Mary likes: Tom likes:

14. User characterization by item preferences Movies Books John likes: Mary likes: Recommendations (multi-domain): Tom likes:

15. User characterization by item preferences Movies Books John 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 items Tom likes:

18. New items or sparsely rated items Tom likes: Recommendations: OOPS!

19. New items or sparsely rated items Tom 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 Recommendation Average 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-domain Collaborative + content based)

23. Aastha Jain

Aastha jain youtube 05 sept 2012.pptx

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Aastha jain youtube 05 sept 2012.pptx