1. 1 Personalized IR Reloaded Xuehua Shen (xshen@uiuc.edu)

2. 2 Self-Adaptive User Profiles for Large Scale Data Delivery U. Cetintemel, M. J. Franklin, C. L. Giles UMCP UCB PSB ICDE2000 A DB conference paper that uses IR techniques

3. 3 Usage of User Profile Personalized Search Information Filtering Push-based Data Delivery

4. 4 Focus of the Presentation Representation, Construction and Maintenance of User Profile Experiment Design

5. 5 Representation of User Profile Multi-Modal Approach,  multiple vectors (clusters) for multiple user interests  correlated vectors

6. 6 Construction of User Profile Given a new judged document Select the most similar vector Decide whether to incorporate doc or create a new vector  If incorporation, do linear update

7. 7 Maintenance of User Profile Merge For the updated vector, if similar vector exists, Do Merge Deletion (Interest Shift) Strength of vector If strength is below deletion threshold, Delete this vector

8. 8 Design of Experiment No Real User: User Simulation No TREC evaluation: Yahoo! Category Training and then Testing

9. 9 Set of Experiments Performance Comparison with existing algorithm RI and RG RI and RG single vector representation

10. 10 Several Parameters of Experiment User Interest (User Profile) Complexity Threshold Change of User Interest  Interest Shifting  Interest Addition  Interest Deletion

11. 11 Results

12. 12 Threshold

13. 13 Interest Shift

14. 14 Summary Multiple vectors for multiple user interests Automatic and Adaptive maintenance (Insertion, Merge, Deletion)

15. 15 Discussion Parameters [Yi Zhang ICML2003] Other Representation of User Profile? Explicit Feedback