1. User Modeling Thoughts on LMs James Allan Center for Intelligent Information Retrieval University of Massachusetts, Amherst September 11, 2002

2. Modeling Interests Valuable to know more about user’s query Valuable to know more about user’s query Experiment Experiment Start with query Start with query Ask user for other words by free-association game Ask user for other words by free-association game Adding those to query improves results Adding those to query improves results A user-model variation on query expansion A user-model variation on query expansion Language model is a probability distribution Language model is a probability distribution How much of user’s free association can be captured? How can it be captured at all? How much of user’s free association can be captured? How can it be captured at all?

3. Modeling Sub-interests User has set of recurring interests User has set of recurring interests Capture interactions and cluster Capture interactions and cluster Compare new material to clusters Compare new material to clusters Expand queries, etc. Expand queries, etc. Some clusters of my Web use (CIIR factored out) Some clusters of my Web use (CIIR factored out) weather, mph, chance, cloudy, calm, shower, … weather, mph, chance, cloudy, calm, shower, … ciir, sigir, croft, allan, callan ciir, sigir, croft, allan, callan false, baggage, aadvantage, …, aboutaa, aaproduct, … false, baggage, aadvantage, …, aboutaa, aaproduct, … tech, edition, cnn, entertain, cnntogo, askcnn, djia, … tech, edition, cnn, entertain, cnntogo, askcnn, djia, … people, talk, question, help, find people, talk, question, help, find

4. Modeling context Some of context is what user is doing Some of context is what user is doing What email was read recently What email was read recently Papers looked at Papers looked at People visiting People visiting Much of this can be captured (in theory) Much of this can be captured (in theory) Can it be used to improve guess of what user wants? Can it be used to improve guess of what user wants?

5. Short-term vs. Long-term Assume past behavior is clustered Assume past behavior is clustered Can adapt model to cluster that seems most likely Can adapt model to cluster that seems most likely Adjust weighting of all known clusters Adjust weighting of all known clusters Ideally need ability to detect new interest Ideally need ability to detect new interest

6. Discussion Points Explicit personalization of interaction to specific user situation Explicit personalization of interaction to specific user situation environment, situation, type of problem environment, situation, type of problem Integrating short-term and long-term models Integrating short-term and long-term models Identifying and effectively using appropriate sources of evidence in user behavior for modeling Identifying and effectively using appropriate sources of evidence in user behavior for modeling Can capture some activities Can capture some activities Is that user knowledge? Can useful information be elicited? Can useful information be elicited? Model user by topics encountered Model user by topics encountered Can then: Can then: Find users with similar interests Improve query accuracy Suggest material of possible interest