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 Application that acquires and stores evidence about a person Identifies consistencies and inconsistencies held about a user Identifies consistencies and inconsistencies held about a user Answer queries of an application about the user Answer queries of an application about the user Assist in the personalisation of the users experience Assist in the personalisation of the users experience

4. Background Deep Map Project Deep Map Project –Group of projects aimed at developing personalised and mobile tourist guides Web Guide Sub Project 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 Explored the characteristics of user modelling systems already developed Developed a generic User Modelling Server Developed a generic User Modelling Server Demonstrated a customisation of the server for the tourist domain Demonstrated a customisation of the server for the tourist domain

7. User Modelling System Background Study Generic Requirements 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 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 is endless Type of client was important when designing storage of data 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 Four types of models –User Models –Usage Models –System Models –Service Models

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

13. User Learning Component Uses Univariate Significance Analysis Uses Univariate Significance Analysis

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

15. Summary ProblemSolution Handle user modelling requirements of the domain Developed user modelling plug in Network Connectivity Directory Storage and distributed storage Act on user events Through event notification No training data initially Use demographic details + learning and modelling