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User Modeling for Personalized City Tours

User Modeling for Personalized City Tours. Josef Fink & Alfred Kobsa. Robert Whitaker. Problem Discussed. develop “a user modelling server that offers services to personalized systems with regard to the analysis of user actions, the representation of assumptions about the

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User Modeling for Personalized City Tours

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  1. User Modeling for Personalized City Tours Josef Fink & Alfred Kobsa Robert Whitaker

  2. Problem Discussed develop “a user modelling server that offers services to personalized systems with regard to the analysis of user actions, the representation of assumptions about the user, and the inference of additional assumptions based on domain knowledge and characteristics of similar users”

  3. What is a User Modelling System • Application that acquires and stores evidence about a person • Identifies consistencies and inconsistencies held about a user • Answer queries of an application about the user • Assist in the personalisation of the users experience

  4. Background • Deep Map Project • Group of projects aimed at developing personalised and mobile tourist guides • Web Guide Sub Project • Gives personalised tour recommendations for the city of Heidelberg

  5. Web Guide Project No Personalisation Personalisation

  6. Work Performed • Explored the characteristics of user modelling systems already developed • Developed a generic User Modelling Server • Demonstrated a customisation of the server for the tourist domain

  7. User Modelling System Background Study • Generic Requirements • Learn interests and preferences of users based on their usage of the application • Predict interests and preferences of users based on those of similar users • Infer additional interests using domain knowledge • Supply an authorised application with current information about the user

  8. User Modelling System Background Study • Additional Requirements need for domain • Need to take into account demographic data • A priori knowledge about the user is generally not available • Explicit acquisition of data at runtime is restricted to a short interview • Adapting to change should be relatively quick

  9. User Modelling Server Architecture

  10. External Clients • Type of client is endless • Type of client was important when designing storage of data • Uses directory management system instead of conventional database

  11. Directory Component • Four types of models • User Models • Usage Models • System Models • Service Models

  12. User Modelling Components • What makes it generic • Components that allow the system to be customised to the specific domain • Examples shown • User Learning Component (ULC) • Mentor Learning Component (MLC)

  13. User Learning Component • Uses Univariate Significance Analysis

  14. Mentor Learning Component • Uses Spearman Correlation • Three Steps • Findings similar users • Selecting Mentors • Computing Predictions

  15. Summary

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