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
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
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
