Your SlideShare is downloading. ×
Personalization and privacy
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Personalization and privacy

301

Published on

Mobile Web Service …

Mobile Web Service

6 Personalization and privacy
學生:陳建富
學號:9577611
資 工 碩 專 一
指導教授:張耀仁

Published in: Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
301
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. 6 Personalization and privacy 學生:陳建富 學號: 9577611 資 工 碩 專 一 指導教授:張耀仁 Mobile Web Service
  • 2. Introduction
    • Objectives of personalization
    • User models
    • Recommender system
  • 3. Objectives of personalization
    • Better serve the customer by anticipating needs.
    • Make the interaction efficient and satisfying for both parties.
    • Build a relationship that encourages the customer to return for subsequent purchases.
  • 4. Objectives of personalization
  • 5. User models
    • Explicit and learned behavior models
    • User stereotypes
    • Natural language interactions
  • 6. User models Explicit and learned behavior models
  • 7. User stereotypes
  • 8. User stereotypes
  • 9. User stereotypes
  • 10. User stereotypes
  • 11. Natural language interactions
    • limited input and output.
    • the mobile terminal will enable simultaneously both text and audio interactions.
  • 12. Recommender system
    • User information items (movies, music, books, news, web pages)
    • The content-based approach
    • The collaborative filtering approach
  • 13. Recommender system
  • 14. Recommender system
  • 15. Recommender system
    • Asking a user to rate an item on a sliding scale.
    • Asking a user to rank a collection of items from favorite to least favorite.
    • Presenting two items to a user and asking him/her to choose the best one.
    • Asking a user to create a list of items that he/she likes.
    Explicit data collection include the following
  • 16. Recommender system
    • Observing the items that a user views in an online store.
    • Analyzing item/user viewing times.
    • Keeping a record of the items that a user purchases online.
    • Obtaining a list of items that a user has listened to or watched on his/her computer.
    Implicit data collection include the following
  • 17. References
    • ^ Parsons, J., Ralph, P., & Gallagher K. (2004). Using viewing time to infer user preference in recommender systems. AAAI Workshop in Semantic Web Personalization, San Jose, California, July.

×