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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

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

1

Foreword
  • “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.”
foreword3

1

Foreword
  • 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?
let s waste some time

1

Let’s Waste Some Time…
  • http://www.lib.utexas.edu/
    • Does it know acronyms? (JCDL)
    • Does it deal with misspellings? (digitul)
    • Can it search on subsets of terms?
    • Does it understand singular/plural?
introduction

2

Introduction
  • Overview of related work:
    • Searcher Behaviors, Collection “Behaviors”
    • Suggestions
    • Social Computing
    • Meta Search Engines
    • Visualizing Search Results
  • Experiment
    • Design
    • Expected Findings
    • Contributions
searcher behaviors

3

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
collection behaviors

3

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
the helping hand suggestions

3

The Helping Hand: Suggestions
  • Misspelled word suggestions
  • Automatic permutation suggestions
  • Acronym recognition

Ebay.com

the helping hand suggestions9

3

The Helping Hand: Suggestions
  • Avoiding the back button
    • Maintaining a consistent direction of flow
  • Minimize swapping between keyboard/mouse
social computing in digital libraries

3

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
social computing in digital libraries11

3

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
meta search engines

3

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

MetaCrystal

meta search engines13

3

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”)
visualizing search results17

3

Visualizing Search Results
  • Maps, Maps, Maps, and Complexity
visualizing search results18

3

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

(But for certain circumstances)

keep the baby not the bathwater

3

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
experiment reaction to

4

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?
experimental design

4

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.
experimental design22

4

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
experimental design23

4

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
experimental design24

4

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?)
experimental design25

4

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)
expected findings

4

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)
contribution to the field

4

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.
conclusions

5

Conclusions
  • 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!
conclusions29

5

Conclusions
  • 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.