Personalization and privacy

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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

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  • 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.