1. 1 Adaptive User Profiling Carolina Bailey (cmbail@essex.ac.uk)

2. 2 User Profiling: Areas Information Retrieval  Personalised Search  Personalised TV Listings  Recommendation Systems  Expert Finding Systems – profile matching Information Filtering  Spam Filters  News Filtering E-Learning  Learner Profiles Intelligent Environments  Intelligent Agents  Behaviour Prediction

3. 3 User Profiling  A user profile can be used like a filter on a set of data, with various sets of data: Search Engine results Environment variables such as lighting settings and temperature settings Recipes News feeds… etc. … any collection of data items that could be personalised  Any information available about the user can be incorporated into the profile Likes, dislikes – specified or implied Various histories e.g. past behaviour, purchasing, browsing, TV watching, bookmarks Disabilities and/or medical details Future data sources

4. 4 User Profiling: Steps

5. 5 Building a User Profile Various Data Source Applications Examples of Data Sources  HTML file (e.g. bookmarks)  XML files  Text files (e.g. rules) Various Methods of Processing Various Representations of Profiles Some Example Personalised Applications and Data Sources…

6. 6 Data Source Applications Search Engine

7. 7 Data Source Applications Question Answering System

8. 8 Data Source Applications Personalised TV

9. 9 Data Source Applications Intelligent Environment

10. 10 Data Sources - XML Recipe Ref: Recipe example from http://www.brics.dk/~amoeller/XML/xml/example.html

11. 11 Data Sources – Fuzzy Logic Example

12. 12 Methods of processing data

13. 13 Representations of Profiles

14. 14 A Global, Unified Profile Potentially, one single profile could be used anywhere, for any application. Currently, the common theme in previous research, is that there is no common theme!  Different data storage methods, data processing methods and algorithms, representation of profiles etc. What is the most efficient of these different methods and processes? Can a user profile from one application be used within another application?

15. 15 A Global, Unified Profile

16. 16 A Global, Unified Profile

17. 17 Global Profile - Considerations Mapping and categorising items to the Global Profile  E.g. a generic term for temperature, heating, radiators etc. Extensible way to add new data (and data sources) to the profile  Textual data  Fuzzy data  Future data items and sources – e.g. SatNav Data storage choices  Main Server  Distributed Transparency of the profile Updating and synchronising

18. 18 Security, Privacy and Legal Implications The User must be in ultimate control! What data should be used in a profile? Purchasing history? Criminal record? Who and what should be allowed access to a profile? The Police? The Government? Could it be used against their wishes? Fine balance between what is good-intentioned personalisation and what is a complete loss of privacy As people lose more and more control of what information is stored about them, their personal freedom may feel encroached upon, resulting in a strong resistance to further developments towards user profiling

19. 19 The End To be continued… cmbail@essex.ac.uk