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Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
Personalization and privacy
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Personalization and privacy

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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
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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 <ul><li>Objectives of personalization </li></ul><ul><li>User models </li></ul><ul><li>Recommender system </li></ul>
  • 3. Objectives of personalization <ul><li>Better serve the customer by anticipating needs. </li></ul><ul><li>Make the interaction efficient and satisfying for both parties. </li></ul><ul><li>Build a relationship that encourages the customer to return for subsequent purchases. </li></ul>
  • 4. Objectives of personalization
  • 5. User models <ul><li>Explicit and learned behavior models </li></ul><ul><li>User stereotypes </li></ul><ul><li>Natural language interactions </li></ul>
  • 6. User models Explicit and learned behavior models
  • 7. User stereotypes
  • 8. User stereotypes
  • 9. User stereotypes
  • 10. User stereotypes
  • 11. Natural language interactions <ul><li>limited input and output. </li></ul><ul><li>the mobile terminal will enable simultaneously both text and audio interactions. </li></ul>
  • 12. Recommender system <ul><li>User information items (movies, music, books, news, web pages) </li></ul><ul><li>The content-based approach </li></ul><ul><li>The collaborative filtering approach </li></ul>
  • 13. Recommender system
  • 14. Recommender system
  • 15. Recommender system <ul><li>Asking a user to rate an item on a sliding scale. </li></ul><ul><li>Asking a user to rank a collection of items from favorite to least favorite. </li></ul><ul><li>Presenting two items to a user and asking him/her to choose the best one. </li></ul><ul><li>Asking a user to create a list of items that he/she likes. </li></ul>Explicit data collection include the following
  • 16. Recommender system <ul><li>Observing the items that a user views in an online store. </li></ul><ul><li>Analyzing item/user viewing times. </li></ul><ul><li>Keeping a record of the items that a user purchases online. </li></ul><ul><li>Obtaining a list of items that a user has listened to or watched on his/her computer. </li></ul>Implicit data collection include the following
  • 17. References <ul><li>^ 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. </li></ul>

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