Session-based search


Exploratory search often takes place over time. Searchers may run multiple queries to understand the collection, to refine their information needs, or to explore various aspects of the topic of interest. Many web search engines keep a history of a user’s actions: Bing makes that history readily available for backtracking, and all major search engines presumably use the click-through history of search results to affect subsequent searches. Yahoo Search Pad diagnoses exploratory search situations and switches to a more elaborate note-taking mode to help users manage the found information.

But none of these approaches makes it easy for a searcher to manage an on-going exploratory search. So what could be done differently? We explore this topic in a paper we’ll be presenting at the IIiX 2010 conference this August. Our paper reviews the literature on session-based search, and proposes a framework for designing interactions around information seeking. This framework uses the structure of the process of exploratory search to help searchers reflect on their actions and on the retrieved results. It treats queries, terms, metadata, documents, sets of queries, and sets of documents as first-class objects that the user can manipulate, and describes how information seeking context can be preserved across these transitions.

We illustrate the framework with an example from a system we are building. The system allows searchers to explore a collection in a session-based manner. For each session, the system keeps track of all queries that were run (typed or relevance feedback) and all documents that were viewed or saved. It uses this information to help searchers make sense of the results accumulated over time.

Queries can be run by typing terms into a text box, or by selecting groups of useful documents for relevance feedback. Documents can be selected in an ad hoc manner, or by grouping all documents found relevant with respect to a particular query.

In addition to the results view, the system maintains a document history view and a query history view. The document history view shows a fused list of documents identified by all queries run in a particular session. It can be sorted in a variety of ways, including based on a fusion score (we use CombMNZ). This approach can surface documents that were retrieved by multiple queries but were never ranked high by any query.

Whenever a document is shown in a results list (whether for a particular query for the entire session), the system also shows that document’s retrieval history in a histogram. The histogram allocates a bar for each query in the query history. The height of the bar indicates how high the document ranked with response to the query. A gap in the histogram indicates that a document was not retrieved by a particular query. This visualization makes it quite easy to tell whether a particular query retrieved new documents or merely re-ranked previously-retrieved ones, and it makes it easy to tell whether some documents are being ranked consistently higher than others.

The system also keeps track of multiple users in each session, making it possible to collaborate with others. Each user’s queries and saved documents are added to the shared history, and color-coding in the query history view and in the document histograms is used to indicate who contributed what. New relevance feedback queries can be run on documents retrieved by other users in a session.

Much remains to be done on this project, including developing strategies for effective evaluation of this tool. Recall and precision measures based on a prior judgments of relevance are hard to apply to this kind of a multi-query environment designed to allow users to pivot around their found objects in a variety of ways. We will need to seek outcomes beyond the search task, and to collect qualitative use data to help us understand the effectiveness of these tools.

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  1. […] This post was mentioned on Twitter by Gene Golovchinsky. Gene Golovchinsky said: Posted "Session-based search" #IIix2010 #hcir […]

  2. Great paper. It’s worth noting that some earlier work myself and others have done about determining session boundaries and episodes (activities within sessions, possibly quite interesting to re-examine for mobile information seeking).

    The combination of page/text analysis to determine sessions and a behavioral model overlay would be a pretty powerful tool for understanding information seeking and information needs. Your ideas contribute a lot to that.

  3. Yeah, while we assume that a session is something that a user creates explicitly, one can certainly apply automated methods to identify session boundaries. You might still want to have explicit user action to identify collaborators, though.

  4. Twitter Comment

    RT @HCIR_GeneG: Posted “Session-based search” [link to post] #IIix2010 #hcir

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  5. Twitter Comment

    great stuff on user search models: RT @HCIR_GeneG: Posted “Session-based search” [link to post] #IIix2010 #hcir

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  6. Twitter Comment

    @donturn Thx 4 pointer to user search models at [link to post] Looking 4 other info seeking behavior relevant to paid & natural search

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    Posted “Session-based search” [link to post] #IIix2010 #hcir

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  8. Twitter Comment

    @brenes For your bibliography. RT @HCIR_GeneG: Posted “Session-based search” [link to post] #IIix2010 #hcir

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  9. […] paper for more on this); there may be opportunities in faceted query expansion research; session-based search that allows the searcher to compare the results of different queries to assess coverage would be […]

  10. […] Interactive Information Seeking via Selective Application of Contextual Knowledge. Gene Golovchinsky, Jeremy Pickens (FX Palo Alto Laboratory, Inc., USA) […]

  11. […] the more recent Querium system, we’re looking at more explicit and more interactive representations of a […]

  12. […] thoughts below are informed by my experiences as an user and by having designed some session-based search interfaces. First of all, the obvious: it is not possible to find all useful information in a […]

  13. […] a collaborative, session-based search tool that I’ve been building over the last few months. Session-based search frames information seeking as an on-going activity, consisting of many queries on a particular […]

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