User Modeling Thoughts on LMs

1. User ModelingThoughts on LMs James AllanCenter for Intelligent Information RetrievalUniversity of Massachusetts, Amherst September 11, 2002

2. Modeling Interests • Valuable to know more about user’s query • Experiment • Start with query • Ask user for other words by free-association game • Adding those to query improves results • A user-model variation on query expansion • Language model is a probability distribution • 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 • Capture interactions and cluster • Compare new material to clusters • Expand queries, etc. • Some clusters of my Web use (CIIR factored out) • weather, mph, chance, cloudy, calm, shower, … • ciir, sigir, croft, allan, callan • false, baggage, aadvantage, …, aboutaa, aaproduct, … • tech, edition, cnn, entertain, cnntogo, askcnn, djia, … • people, talk, question, help, find

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

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

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