Lecture 12 User Modeling

1. Lecture 12 User Modeling • Topics • Basics • Example User Model • Construction of User Models • Updating of User Models • Applications

2. Basics • User preference vs profile vs model • A user model is a specification of user characteristics aiming to facilitate reasoning about his needs, preference andbehavior. • User characteristics include background, mental states, interests, interaction patterns, etc. • Modelling methods • Knowledge-based approach • Machine learning approach

3. Basics • Knowledge-based approach • Explicitly express user (or user group) characteristics in KB in terms of formal KR, e.g., FOL, rules, etc. • Knowledge acquisition (KA): questionnaire, interview, observation. • Reasoning: Use KB to reason about user needs or difficulties • Characteristics of knowledge-based approach • Formal representation • Reasoning capability

4. Basics • Characteristics of knowledge-based approach • KA limitation: Hard to comprehend complete and consistent user characteristics • Model updating is equivalent to KB evolution: very hard to handle concept drift problem

5. Basics • Machine learning approach • Learn user characteristics from user behavior including user interaction patterns, user feedback, etc. • Example learning mechanisms • KNN (K-Nearest Neighbor)/ K-Means: learn clusters based on similarity of vector spaces • Decision tree: learn classification rules based on user reviewed solutions and information gain

6. Basics • Example learning mechanisms • Naïve Bayesian: construct a Bayesian classifier based on Bayesian rule according to categorized user feedback on proposed solutions • Bayesian network: construct a Bayesian network to represent relationships among user’s actions, goals, and system events/states. • CBR: construct a case library to support solution prediction

7. Basics • Characteristics of machine learning approach • Need high-quality training data • Need labeled data (from user feedback) if using classification techniques • Model updating is easier but hard to main intricate balance between long term interests drift and short term interests drift • High time complexity for on-line processing

8. Example User Model • Six categories of user characteristics

9. Example User Model • Example information of Background Knowledge and User Idiosyncrasy

10. Example User Model • Example information of Interaction Preference

11. Example User Model • Example information of Solution Presentation

12. Example User Model • Example information of User Interests • Explicit user feedback • Interesting degree • Comprehension degree • Satisfaction degree • Definite/most/average/some/none • Query history • Solution visit history • Query time/ solution visit time/ visit sequence/ .. • Hyperlinks visit history

13. Construction of User Models • User Stereotype • Collect existing user models and cluster them into several groups according to the six categories of user characteristics • Define a specification for each group, working as the stereotype for the user group • Or Experts hand-code stereotypes • Collaborative user modeling • Fast initialization of a user model for a new user

14. Construction of User Models • How to do collaborative user modeling • Get new user’s basic information through a simple questionnaire session • Cluster the user into one of the user groups • Use the corresponding stereotype as his initial user model • Update user stereotypes after a sufficient number of user models are updated

15. Construction of User Models • Expert-group stereotype

16. Updating of User Models • Basic concepts • Query session (QS) • From query posted up to feedback returned • Interaction session (IS) • From user login up to logout • Updating of Background Knowledge • Update Domain Proficiency Table according to explicit user comprehension feedback and concept difficulty degree as recorded in domain ontology (QS) • Learn user interests fromImplicit User Interests (several IS’s)

17. Updating of User Models • Updating of User Idiosyncrasy • Update Terminology Table by analyzing user-preferred terms in a given query (QS) • Updating of Interaction Preference • Update each query mode according to the user interaction pattern (QS) • Update each recommendation mode according to the FAQ-Selection History (QS) • Updating of Solution Presentation • Update each presentation mode and corresponding presentation ratio according to the FAQ-selection history (QS)

18. Updating of User Models • Updating of User Interests • Record returned user evaluation in Explicit User Feedback (QS) • Record the user interaction information in Implicit User Interests (QS) • Updating of user stereotypes • Calculate a statistic (e.g., average) for each user characteristic from all user models • Redistribute user models to user stereotypes according to the new statistics • Recalculate representative values in each user stereotype

19. Applications • Query processing • User intention extraction according to user interests • Query extension with user interests • Agent-based computing • Task delegation, comprehension and processing • Trust development • E-learning • Construction of student model • E-commerce • Trust development