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Your Search Returned 0 Results: Improving Digital Library Search Tools. Paul Aumer-Ryan School of Information The University of Texas at Austin November 29, 2006. 1. Foreword. “No Results Found” can have several meanings:

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Your search returned 0 results improving digital library search tools l.jpg

Your Search Returned 0 Results: Improving Digital Library Search Tools

Paul Aumer-Ryan

School of Information

The University of Texas at Austin

November 29, 2006

Foreword l.jpg



  • “No Results Found” can have several meanings:

    • “The explicit assemblage of characters you submitted does not occur anywhere in our index of items in our collection.”

    • “We don’t understand what you just typed.”

    • “We understand some of the things you typed, but not all of them.”

    • “We have what you are looking for, but we call it something else.”

    • “We don’t have what you are looking for.”

    • “Go away.”

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  • How is a patron supposed to determine which meaning is being conveyed?

  • “No Results Found” seems pretty authoritative and final; it’s a statement of fact, and it’s coming from a computer.

  • In a world where information overload has become cliché, how do we react to the opposite?

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Let’s Waste Some Time…


    • Does it know acronyms? (JCDL)

    • Does it deal with misspellings? (digitul)

    • Can it search on subsets of terms?

    • Does it understand singular/plural?

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  • Overview of related work:

    • Searcher Behaviors, Collection “Behaviors”

    • Suggestions

    • Social Computing

    • Meta Search Engines

    • Visualizing Search Results

  • Experiment

    • Design

    • Expected Findings

    • Contributions

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Searcher Behaviors

  • Models of Search Behavior:

    • Deep Divers vs. Broad Scanners vs. Fast Surfers

    • Query refiners vs. “I’m Feeling Lucky!”-ers

    • Expert vs. Novice

    • Seeking vs. Encountering vs. Exploring

    • Digital Libraries vs. The Web

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Collection Behaviors

  • Different searchers have different wants, and different collection types call for different search tools

  • Models of collection “behavior”:

    • Small vs. Large

    • Homogeneous vs. Heterogeneous

    • Interrelated vs. Distinct

    • Single medium vs. Many media

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The Helping Hand: Suggestions

  • Misspelled word suggestions

  • Automatic permutation suggestions

  • Acronym recognition

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The Helping Hand: Suggestions

  • Avoiding the back button

    • Maintaining a consistent direction of flow

  • Minimize swapping between keyboard/mouse

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Social Computing in Digital Libraries

  • Personalization

    • Search results are tailored based on the patron’s history…

    • With obvious privacy implications

  • Peer Recommendations

    • At the very least, links that were followed and/or rated highly by searchers using the same search terms will be preferred

    • More involved: results from peers with similar interests will be preferred...

    • With obvious privacy implications

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Social Computing in Digital Libraries

  • Patron Tagging

    • Objects in the DL can be tagged by patrons, and these tags can be searched

  • Thumbs Up / Thumbs Down

    • A simple, patron-driven measure of the applicability of a document to a given search term

  • Popularity Rankings

    • “Popular” documents ranked higher; could be measured in many ways

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Meta Search Engines

  • If one search engine returns no results, how about three or five?


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Meta Search Engines

  • Problems with aggregators:

    • Always done by a 3rd party

    • Relies on all engines being available and up-to-date

    • Only as fast as the slowest member

    • Adds a layer of complexity (Schwartz’s “The Paradox of Choice”)

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Visualizing Search Results

  • Relational Maps

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Visualizing Search Results

  • Topic Maps

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Visualizing Search Results

  • Concept Maps

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Visualizing Search Results

  • Maps, Maps, Maps, and Complexity

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Visualizing Search Results

  • In general, visualization tends to deal with too much complexity, rather than too little

(But for certain circumstances)

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Keep the Baby, Not the Bathwater

  • Rather than performing an end-run around our problem (e.g., visualization maps), the focus here is on classic textual search and retrieval

  • “No Results Found” is applicable to all types of searches, but visualization adds another layer of complexity that we don’t need to deal with now

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Experiment: Reaction to Ø

Ø = No Results Found

  • Broad Questions:

    • What are the affective implications of encountering a null result set?

    • What impact does the digital library interface have on the interpretation of its contents?

  • Focused Question: After encountering a null result set, how do participant’s emotional responses affect further searches on the same topic?

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Experimental Design

  • A mock digital library will be created:

    • Participants will interact with it via a simple search tool, which they will be told they are evaluating;

    • Participants will be given a topic to search for and several questions to answer regarding that topic;

    • The digital library will contain a small set of results pertaining to that topic.

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Experimental Design

  • Participants will be divided into 3 groups:

    • Control Group: Get appropriate results from their first search term;

    • Experimental Group 1: Encounter Ø once, then subsequent search will return appropriate results;

    • Experimental Group 2: Encounter inappropriate results.

  • There will ideally be at least 50 people in each group

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Experimental Design

  • Before searching the digital library, Participants will:

    • Answer a set of demographic questions

    • Rate their general mood (affect)

    • Rate their familiarity with computers, digital libraries, and research

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Experimental Design

  • After evaluating the results in their own fashion, Participants will:

    • Answer a set of questions confirming comprehension;

    • Rate the authoritativeness of the results they found;

    • Rate their impressions of the digital library and the search tool;

    • Rate their general mood (affect)

    • (Would behavioral measures, e.g. skin conductivity and heart rate, be worthwhile during the seeking process?)

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Experimental Design

  • Data Collected:

    • Pre-test questionnaires (demographics, baseline affect, familiarity measures)

    • Experimental data (time-on-task, search queries, number of mouse clicks, back button presses, etc., and possible behavioral measures)

    • Post-test questionnaires (authoritativeness, opinion of DL, affect)

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Expected Findings

  • Participants who encounter Ø will:

    • Take more time completing the task (of course);

    • Rank the results as less authoritative;

    • Have a lower opinion of the search tool;

    • Exhibit more negative affect (frustration, anger, distress).

  • Participants who encounter inappropriate results are expected to be similar.

  • Novice users are expected to be more susceptible than expert users (see Chesney’s First Monday paper)

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Contribution to the Field

  • This study hopes to elucidate the dangers of “no results found” responses by showing the actual effects on digital library users;

  • If Participants do indeed see results following Ø as less authoritative, it means the contents of a digital library are being evaluated not on their own merit, but by the interface’s effect on them;

  • If Participants have a lower opinion of a digital library because it returns Ø, then they are likely to go elsewhere;

  • If Participants exhibit more negative affect because of Ø, that’s just generally bad.

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  • Empty search result pages tend to get ignored in the design and testing process:

    • Because they are not destinations;

    • They are just fleeting error messages;

    • They have little impact other than saying, “Try Again”; and our captive users have no choice, right?

    • Patrons won’t be spending any time there anyway;

    • Testers are so familiar with the interface that they hardly ever see them.

  • Ignore them no more!

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  • In short, it is no longer enough to simply “put a digital library out there” for consumption; we need to make sure that we aren’t misleading patrons by saying we don’t have what we actually do have.