1. Artificial Intelligence and Case-Based Reasoning Computer Science and Engineering Mälardalen University Västerås, Sweden @mdh.se Mikael Sollenborn, CSL, Eyescream AB

2. What is Case-Based Reasoning A model of human problem solving and reasoning A method for building ”intelligent” computer systems

3. CBR as a model of human reasoning Most of the problems a decision maker has to deal with aren’t unique When solving new problems, we tend to reuse solutions to similar problems People generally prefer examples to rules (and besides, rules are generally not available)

4. CBR as a method of building ”intelligent” systems stores previous experience in a case-library solves new problems by: 1) retrieving similar cases from the case library 2) reusing full or part of the cases in the context of the new problem 3) adapting the solution to match current problem 4) storing new case. i.e. new problem and solution in case library

5. General architecture of a CBR system Learning from past cases, building up experience, improving performance and adapting to changing environment.

6. CBR Essential works R. Schank (1982): Dynamic memory: a theory of learning in computers and people C.K. Riesbeck, R. Schank (1989): Inside Case-Based Reasoning J. Kolodner (1993): Case-Based Reasoning I. Watson (1995): Progress in Case-Based Reasoning

7. Personalisation Personalisation prototype developed for Eyescream AB for Masters Thesis How to create applications/web pages whose behaviour changes dynamically according to user preferences –Information filtering (handling the information overload problem) –User Modelling (what we know about the user, and how to utilise this knowledge)

8. What is Information Filtering From a large amount of data/items, extract the interesting parts Used in Recommender Systems, typically using –Content-based filtering –Collaborative filtering

9. Recommender (hybrid) systems 1. Record the behaviour of a large number of people 2. Select a number of users whose past behaviour is similar to the current user 3. Make recommendations based on the similar users preferences and the user’s own preferences

10. Content-based filtering (CBR) Items are cases Category belonging and other meta- data is the problem-description of a case Compare current user preferences with items, selecting the closest matching ”solutions”

11. Automated collaborative filtering (ACF) Based on ”peer reviews” Similar users recommend items (unknowingly) to each other

12. Essential works Collaborative filtering –U. Shardanand, P. Maes(1995): Social information filtering: algorithms for automating ’word of mouth’ –Hill et.al(1995): Recommending and evaluating choices in a virtual community of use Content-based filtering –K. Lang(1995): Newsweeder: learning to filter netnews –Pazzani et.al(1996): Syskill & Webert: Identifying interesting web sites Recommender systems –H. Kautz(editor)(1998): Recommender systems. Papers from 1998 workshop –I. Soboroff et.al(editors)(1999): ACM SIGIR’99 Workshop on Recommender Systems: Algorithms and Evaluation

13. Problems with recommender systems Response time (all reasoning done online) Poor performance in domains where items are often added and removed Crude recommendations, using only two dimensions (users, items)

14. Handling response time Precalculating similarity metrics –with 100 000 users, it may still not be good enough Collective models, created offline using clustering techniques –faster retrieval –will loose accuracy in the process?

15. Handling dynamic domains Identify fine-grained item categories –Categorise each new item by one or more categories, possibly using text extraction techniques –When new items arrive, systems knows current user or similar users attitude towards the item categories

16. Adding rating dimensions R: Users x Items  Rating R movies (John, Nosferatu 5)  10 Multi-dimensional: users, items, time of day, time of year etc. –R: D 1 xD 2 x…....D n  Rating R movies (John, Nosferatu 5, 15.00, 24 Dec)  1 …which leads us to...

17. User Modelling How to gather information about a user or users, his/her/their preferences etc. How to use the gathered information to help satisfy the users needs

18. User Modelling essential works A.J. Kok, 91: A review and synthesis of user modelling in intelligent systems A. Kobsa, 93: User Modeling, recent work, prospects and hazards

19. Acquiring user models: asking the user Invasive Rich information, but –users could be giving incorrect answers –users are easily annoyed How do we ask the right questions?

20. Acquiring user models: tracking the user Non-invasive Observe user behaviour –What is clicked –How long is the information viewed –In what context are decisions made –… Hard to evaluate, noisy information

21. User Modelling for WWW Invasive –Explicit rating –Personal questions Non-Invasive –Clicks –Click context –Time read –Following mouse movements –Personal info through ordering forms

22. User modelling for learning systems Users are generally more positive towards invasive techniques (if they attain positive changes) Users will stay longer and will be generally more interested in the information content

24. Summary & Conclusions Methods and techniques from Artificial Intelligence have already proven to be useful in many application areas and have still much to offer. Case-Based Reasoning and User Modelling is a promising combination, especially in internet/intranet applications.