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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
Introduction <ul><li>Objectives of personalization  </li></ul><ul><li>User models </li></ul><ul><li>Recommender system </l...
Objectives of personalization <ul><li>Better serve the customer by anticipating needs. </li></ul><ul><li>Make the interact...
Objectives of personalization
User models <ul><li>Explicit and learned behavior models </li></ul><ul><li>User stereotypes </li></ul><ul><li>Natural lang...
User models Explicit and learned behavior models
User stereotypes
User stereotypes
User stereotypes
User stereotypes
Natural language interactions <ul><li>limited input and output. </li></ul><ul><li>the mobile terminal will enable simultan...
Recommender system <ul><li>User information items (movies, music, books, news, web pages)  </li></ul><ul><li>The content-b...
Recommender system
Recommender system
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 col...
Recommender system <ul><li>Observing the items that a user views in an online store. </li></ul><ul><li>Analyzing item/user...
References  <ul><li>^  Parsons, J., Ralph, P., & Gallagher K. (2004). Using viewing time to infer user preference in recom...
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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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Transcript of "Personalization and privacy"

  1. 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. 2. Introduction <ul><li>Objectives of personalization </li></ul><ul><li>User models </li></ul><ul><li>Recommender system </li></ul>
  3. 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. 4. Objectives of personalization
  5. 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. 6. User models Explicit and learned behavior models
  7. 7. User stereotypes
  8. 8. User stereotypes
  9. 9. User stereotypes
  10. 10. User stereotypes
  11. 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. 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. 13. Recommender system
  14. 14. Recommender system
  15. 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. 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. 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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