12 -1 Lecture 12 User Modeling Topics –Basics –Example User Model –Construction of User Models –Updating of User Models –Applications

12 -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 and behavior. User characteristics include background, mental states, interests, interaction patterns, etc. Modelling methods –Knowledge-based approach –Machine learning approach

12 -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

12 -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

12 -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

12 -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

12 -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

12 -8 Example User Model Six categories of user characteristics

12 -9 Example User Model Example information of Background Knowledge and User Idiosyncrasy

Example User Model Example information of Interaction Preference

Example User Model Example information of Solution Presentation

Example User Model Example information of User Interests Explicit user feedback 1.Interesting degree 2.Comprehension degree 3.Satisfaction degree 4.Definite/most/average/some/none 1.Query history 2.Solution visit history 3.Query time/ solution visit time/ visit sequence/.. 4.Hyperlinks visit history

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

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

Construction of User Models Expert-group stereotype

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 from Implicit User Interests (several IS’s)

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)

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

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