Exploratory search is a difficult activity that requires iterative interaction. This iterative process helps the searcher to understand and to refine the information need. It also generates a rich set of data that can be used effectively to reflect on what has been found (and found useful). While traditional information retrieval systems have focused on organizing the data that was retrieved, in this paper, we describe a systematic approach to organizing the metadata generated during the search process. We describe a framework for unifying transitions among various stages of exploratory search, and show how context from one stage can be applied to the next. The framework can be used both to describe existing information seeking interactions, and as a means of generating novel ones. We illustrate the framework with examples from a session-based exploratory search system prototype.

1. Interactive Information Seeking via Selective Application of Contextual Knowledge Gene Golovchinsky and Jeremy Pickens FX Palo Alto Laboratory, Inc.

4. Evolution of Search Interaction Start Formulate query Run query Examine results Save some docs Stop Examples: Early batch retrieval systems Modern web search

5. Evolution of Search Interaction Start Formulate query Run query Examine results Save some docs Done? Stop N Y

6. Evolution of Search Interaction Start Formulate query Run query Examine results Save some docs Done? Stop Revisit query? N Y N Y

7. Evolution of Search Interaction Start Formulate query Run query Examine results Done? Stop Revisit query? N Y N Y Save some docs Relevance feedback

8. Evolution of Search Interaction Start Formulate query Run query Examine results Done? Stop Revisit query? Save some docs Relevance feedback Select, fuse, filter, pivot N Y N Y N

11. Information Retrieval as Transitions More examples in the paper Term suggestion Query substitution Traditional IR, pseudo-relevance feedback “ I am feeling lucky” Query Query expansion Relevance feedback Personalized recommendation Document set Query expansion “ More like this” relevance feedback Link following Document Query Document set Document From/to

17. Run a query…

18. Judge top 10 documents…

19. Run a second query & judge docs

20. Run a third query & judge docs

21. Run relevance feedback query…

22. Deselect two queries…

23. Examine history, find more relevant docs

24. Filter to see unread documents

28. Questions?