Insights on User Modeling for Information Retrieval

1. User Modeling for IR Nicholas J. BelkinSCILS, Rutgers University nick@belkin.rutgers.edu IR & LM Workshop

2. What’s Been Done in UM for IR • Queries as representations of user’s information “need” – model of the topic • LM could be/has been applied to this purpose (but usually there’s too little language) • Type of user – novice/experienced in topic, in IR; individual differences • Usually explicitly elicited, hard to see what LM has to offer IR & LM Workshop

3. What’s Been Done in UM for IR • User stereotypes • Based on relating other users’ behaviors to the current user’s (e.g. Amazon recommendations). Some possibilities for LM here IR & LM Workshop

4. What’s Not Been Done in UM for IR • User goals • At various levels • User situation • Environment, context • Type of information problem • User knowledge (other than categorical) • Long-term models for different interests / topics IR & LM Workshop

5. What Should be Done in UM for IR • Implicit sources of evidence for all types of models • Dynamic models for single information seeking episodes, and for sequences of episodes • Discriminating between different topic, type, and goal models IR & LM Workshop

6. Challenges for UM in IR • Explicit personalization of interaction to specific user situation • Integrating short-term and long-term models • Identifying and effectively using appropriate sources of evidence in user behavior for modeling IR & LM Workshop