Reading Summary And Analysis Discussion Post: Exploring Information Systems Total pages: two or more There are three readings that need to be read, please

Click here to Order a Custom answer to this Question from our writers. It’s fast and plagiarism-free.

Total pages: two or more

There are three readings that need to be read, please read through them and write summary and analysis according to the questions below for each reading. Please write each question separately by using 1) 2) 3), and also separately each reading with its title. Thank you so much!

  1. A brief summary of the key argument, problem, or issue
  2. Suggesting the significance of the piece (how it contributes to our understanding of this topic within our class’s broad study of human information interaction)
  3. Posing one or more questions that you would like to probe about this reading or any other combination of strategies to get the group discussion going

Introduction

Inherent in the concept of interactive information retrieval is the notion that we
interact with some search user interface (SUI) beyond the submission of an
initial query. Perhaps the most familiar SUI to many is the streamlined
experience provided by Google, but many more exist in online retail, digital
archives, within-website (vertical) search, legal records and elsewhere. Amazon,
for example, provides a multitude of different features that together make a
flexible, interactive and highly suitable gateway between users and products.

The aim of this chapter is to provide a framework for thinking about the
elements that make up different SUI designs, taking into account when and
where they are typically used.

Search: the way we usually see it
The SUI that many people now see daily is Google, and Figure 8.1 overleaf
shows the 14 notable SUI features it provides for users on its search engine
results page (SERP). The most common feature searchers expect to see is the
query box (#1 in Figure 8.1), which in Google provides a maintained context so
that the query can easily be edited or changed without going to the previous
page. Searchers are free to enter whatever they like, including special operators
that imply specific phrases or make sure certain words are not included. The
second most obvious feature is the display of results (#2), which is usually1
ordered by how relevant they are to the search terms. Results typically highlight
how they relate to the search terms by showing parts in bold font. Users are
typically able to view additional results using the pagination control (#3).

We also see many control and modifier SUI features. Google provides fixed
options across the top (#4) and relevant options down the left (#5) for

139

8
●●●

Interfaces for information retrieval

Max Wilson

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

INTERACTIVE INFORMATION SEEKING, BEHAVIOUR AND RETRIEVAL

140

specializing the search towards certain types of results. Further, Google allows
users to restrict their results (#6), or change how they are shown (#7). It is typical
for search engines to provide an advanced search to help define searches more
specifically (#8). Finally, most search engines provide recommendations for
related queries (#9).

Google also provides extra information, such as an indicator on the number
of results found (#10), and information about when you may have made an
error (#11). Finally, Google also provides personalizable features that are
accessible when signed in (#14), such as settings (#13) and information about
your prior searches (#12).

A starting framework for thinking about SUI designs
Broadly, we can break the elements of a SUI, like those discussed in the Google
example above, into four main groups:

• input features – which allow the user to express what they are looking for
• control features – which help users to modify or restrict their input
• informational features – which provide results or information about results
• personalizable features – which relate specifically to searchers and their

previous interactions.

Figure 8.1 Fourteen notable features in the Google search user interface

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

These groups are highlighted in zones in Figure 8.2 (input as 1 and 8, control as
4, 5, 6, 7 and 9, informational as 2, 3, 10 and 11, personalizable as 12, 13 and 14),
and will be revisited throughout the chapter as other search interfaces provide
different features in these groups. Often new SUIs or SUI features innovate in
one of these groups. Finally, it is important to note that these groups can
overlap. Informational features are often modified by personalizable features,
for example, and some features can act as input, control and informational
features.

Early search user interfaces
A brief early history

The roots of information retrieval systems are in library and information science.
In libraries, books are indexed by a subject-oriented classification scheme and
to find books we interact with the physical spaces, signposting, and librarians
within them. Yet the study of information retrieval was motivated by the
development of computers in the 1960s, which could automatically perform one
of the tasks that librarians do: retrieve a document (or book). The interface with
computers, however, was with punch cards at first, and then command lines
sometime after. Immediately, we can see the model kind of support we wanted
to provide to users (a librarian) but were so far limited by technology.

141

WILSON • INTERFACES FOR INFORMATION RETRIEVAL

Figure 8.2 The Google SUI zoned by the different types of feature categories

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

Conversation and dialogue

Given the user interface limitations, and the influence of librarianship, some of
the initial SUIs were modelled around conversations or ‘dialogues’. In
analysing, for example, the roles, questions and answers that took place in
conversations between visitors and librarians (Winograd and Flores, 1986), early
researchers developed question and answer style SUIs. Figure 8.3 shows an
early command-line dialogue-style system introduced in the 1970s (Slonim,
Maryanski and Fisher, 1978), which tried to help users describe what they were
searching for. These SUIs typically asked the searchers for any information they
already had about what they wanted, so that when it came to performing the
search (which could last a number of minutes or hours even) it was more likely
to return the correct result.

This conversational style was analysed for some time, and was also influenced
by those interested in artificial intelligence and natural language processing. As
technology improved and results were returned faster, the emphasis of the
conversational perspective moved towards modelling a continued dialogue
over multiple searches within interactive information retrieval. The MERIT
system (Belkin et al., 1995), for example, was designed based on a much more
flexible, continuing, conversation model.

Browsing

Another early type of system, still using command-line interaction, supported
‘browsing’. Similar to the initial dialogue-based systems, browsing systems
like the 1979 BROWSE-NET (Palay and Fox, 1980) in Figure 8.4 (on page 144)
presented different modes to scan through databases and provided options for
different ways of accessing the documents. Again, we see these browsing style
systems appear over the course of interactive information retrieval design,
although in 1983 research identified that people ‘browsed’ less on the early
online newsgroups. Geller and Lesk (1983) hypothesized that this may have
been because people often knew more about what was in a fixed dataset than
in the oft-changing web collection we have now. Despite this hypothesis, we
later saw the rise of website directories, like the Yahoo! Directory. Directories,
while still available, were never as successful as web search engines, perhaps
providing evidence for Geller and Lesk’s hypothesis. More recently, we see
browsing interfaces appear within individual websites, as discussed further in
the discussion of faceted browsing in the section ‘Faceted metadata’.

INTERACTIVE INFORMATION SEEKING, BEHAVIOUR AND RETRIEVAL

142

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

Form filling

As SUIs became more directly interactive, with the onset of commercially
available graphical user interfaces2 in the early 1980s, the common paradigm we
see today of ‘form filling’ became more popular. This advanced the conversational
response SUIs, which took input over time from a series of questions, by providing

143

WILSON • INTERFACES FOR INFORMATION RETRIEVAL

Figure 8.3 An early command-line dialogue-style system (Slonim, Maryanski and Fisher,
1978).
Copyright © 1978 ACM, Inc. doi>10.1145/800096.803134. Reprinted by
permission.

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

INTERACTIVE INFORMATION SEEKING, BEHAVIOUR AND RETRIEVAL

144

all the data entry fields spatially.
Although ‘form filling’ includes normal
keyword searching, this technique
allowed systems to present all the fields
that could be individually searched in a
way that we now commonly call an
advanced search. The EUROMATH
system, shown in Figure 8.5 designed by
McAlpine and Ingwersen (1989), has a
custom form highlighting all the fields
that can be searched individually or in
combination.

Boolean searching

One advance in the algorithmic technol –
ogies was to process Boolean queries, so
that we could ask for information about
‘Kings OR Queens’, and get a more
comprehensive set about, in this case,
Monarchs. This technological advance
was made before the majority of SUI
develop ments, as can be seen in Figure
8.5. The advent of GUIs, however,
provided an opportun ity to help people
construct Boolean queries more easily
and visually. The STARS system (Anick
et al., 1990), shown in Figure 8.6,
allowed users to organize their query in

a 2D space, where horizontal space represented ‘AND’ joins, and anything aligned
vertically were ‘OR’ joins. Like all these early ideas, Boolean searching is still
prevalent in our modern interactive information retrieval SUIs, including Google
(see Figure 8.1); the ‘-’ before the word is equivalent to a Boolean NOT, in this case.

Summary

The initial advances in information retrieval were typically made in
technological improvements. Consequently, these SUI advances in the early
days related mainly to the input SUI features, with the exception of some
advances (like the browsing and form filling) which provided information about
the structure of the data, making them also contribute to the informational SUI

Figure 8.4 An early browsing interface
for databases (Palay and Fox, 1981).
Copyright © 1980 ACM, Inc. Reprinted by
permission.

Figure 8.5 The EUROMATH interface
(McAlpine and Ingwersen, 1989).
Copyright © 1989 ACM, Inc.
doi>10.1145/75334.75341. Reprinted by
permission.

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

features. Other informational advances included simple highlighting in a result
where it matched the query, as shown in Figure 8.7 where the horizontal bar at
the bottom indicated where in a book any search terms appear. The onset of
GUIs meant that SUIs became more interactive, with Pejtersen’s fiction browser
(Pejtersen, 1989) presenting an explorable-world view of a bookshop, as shown
in Figure 8.8 overleaf. Pejtersen’s fiction bookshop allowed users to browse the
bookshop using different strategies, where the figures shown are engaging in each
strategy. We were not yet, however, engaging in what we now call interactive

145

WILSON • INTERFACES FOR INFORMATION RETRIEVAL

Figure 8.6 The STARS system (Anick et al.,1990).
Copyright © 1990 ACM, Inc. doi>10.1145/96749.98015.
Reprinted by permission.

Figure 8.7 Use of highlighting for terms that match a query (Teskey, 1988).
Copyright © 1988 ACM, Inc. doi>10.1145/62437.62481.
Reprinted by permission.

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

INTERACTIVE INFORMATION SEEKING, BEHAVIOUR AND RETRIEVAL

146

information retrieval, where we consider interactive information retrieval to be
the ongoing interaction over multiple searches to reach a goal, rather than the
single search that is still often considered in information retrieval.

The onset of modern interactive information retrieval SUIs

The onset of modern interactive information retrieval SUIs began around the
time that we first saw web search engines like AltaVista,3 but before Google
was launched. One of the first studies to demonstrate that there were
significant and specific benefits to interactive information retrieval, where users
actively engage in refining and submitting subsequent queries, was provided
by Koenemann and Belkin (1996). Using a query engine that was popular at
the time called INQUERY, Koenemann and Belkin built the RU-INQUERY SUI,
shown in Figure 8.9 (b). Searchers could submit a query in the search box at
the top left, and see a scrollable list of results on the right hand side. The current
query was then displayed in the box underneath the search box. The full text
of any selected result was displayed beneath the results on the right. The RU-
INQUERY interface had hidden, visible, and interactive relevance feedback
terms; the interactive terms provided the most effective support for users.

Figure 8.8 Pejtersen’s fiction bookshop (Pejtersen, 1989).
Copyright © 1989 ACM, Inc. doi>10.1145/75334.75340.
Reprinted by permission.

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

The experiment was built to leverage relevance feedback (discussed in Chapter
6, ‘Access Models’), which used key terms from the results marked as ‘relevant’,
using the check boxes, and added them to the search to get more precise results.
To demonstrate the benefits of interaction in information retrieval, three altern –
ative versions were developed:

• opaque – provided the typical relevance feedback experience that was
common at the time, where terms from the selected relevant documents
were added, but there was nothing in the SUI to display what those
additional terms were (Figure 8.9(b))

• transparent – provided a similar experience to the opaque version, except
that the added terms were made visible in the ‘current query’ box

• penetrable – allowed the users to choose additional terms from the relevant
documents; the keywords associated with the relevant documents were
listed in a separate box below the ‘current query’ box (Figure 8.9 (a)), and
could be added to the current query box manually.

While all three experimental versions provided improved support within a task-
based user study, the most interactive penetrable version provided statistically
significant improvements and did not significantly increase the time involved
in searching. When analysed according to the framework described in the
section ‘A starting framework for thinking about SUI designs’ above, this study
showed the initial value of having control SUI features that help people modify
and manipulate a search.

147

WILSON • INTERFACES FOR INFORMATION RETRIEVAL

Figure 8.9 The RU-INQUERY interface (Koenemann and Belkin, 1996).
Copyright © 1996 ACM, Inc. doi>10.1145/238386.238487.
Reprinted by permission.

(a) Penetrable condition

(b) Opaque condition

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

INTERACTIVE INFORMATION SEEKING, BEHAVIOUR AND RETRIEVAL

148

Modern search user interfaces and features

This section covers many of the more modern advances in SUI designs, and is
structured according to the framework described in the section ‘A starting
framework for thinking about SUI designs’. It begins by discussing input
features, before moving on to control, informational and personalizable features.

Input features

While there have been many technical advances in the processing of user
queries and matching them against documents, the plain white search box has
remained pleasingly simple. This section begins by examining the design of the
search box, before moving on to other input methods.

The search box

The search box pervades SUIs and searchers can feel at a loss when they do not
have a small white text field to spill their search terms into. The search box has
many advantages:

• Flexibility – It is extremely flexible (assuming the technology behind it is
well made), uses the searcher’s language and the searcher can be as
generic or specific as they like.

• An informational feature – As well as being primarily used as an input
feature, the search box can – and should – be used as an informational
feature. When not being used to enter keywords, the search box should be
informing the user of what is currently being searched for.

• The auto-complete function – This can help people avoid entering unproductive
search queries. By providing information to the user as they query, auto-
complete helps make the search box a better informational feature as well as
an input feature. Auto-complete can be rich with context, with the Apple
website providing images, short descriptions and even prices, as can be seen
in Figure 8.10 (a). Furthermore, auto-complete can be personizable, as with
Google in Figure 8.10(b), which shows queries the searcher has used before.

• Operators and advanced search – The keyword search box itself has only
really had minor visual changes, with some suggesting this may affect the
number of words people put in their query. Regardless, studies indicate
that searchers submit between two- and three-word queries (Jansen,
Spink and Saracevic, 2000; Kamvar et al., 2009), and around 10% of
searchers use special operators to block certain words or match explicit
phrases. Advanced search boxes, when implemented well, can help guide
people towards providing more explicit queries in the search box.

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

149

WILSON • INTERFACES FOR INFORMATION RETRIEVAL

(a) Apple – shows lots of contextual data (b) Google – prioritizing previous searches

Query by example

There is a range of searching systems that take example results as the input.
One example commonly seen in SERPs is a ‘More Like This’ button, which
returns pages that are related to a specific page. While these could be seen as
control examples, an example demonstrator called Retrievr 4 lets searchers
sketch a picture and returns similar pictures. Similarly, services like Shazam5
use recorded audio as a query to find music. Shazam and Retrievr are examples
that are explicitly query by example input features, while others can be seen as
input and/or control.

Adding metadata

While there have been some variations in how we enter information into a
search box, the alternative is typically to present useful and usable metadata to
the users as an input feature. The presentation and use of metadata in SUIs,
however, can be very hard to delineate in its contribution between input,
control, informational and personalizable features. Indeed, well designed use
of metadata can serve as a feature in each of these feature types. Presented on
the front page of a SUI, categories can, for example, allow the searcher to input
their query by browsing. If a searcher can filter their keyword search, or make
sub-category choices, then metadata can quickly become a control feature.
Further, if results are accompanied by how they are categorized, then metadata

Figure 8.10 Examples of auto-complete

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

can become an informational feature too; research has shown this to be popular
with searchers (Drori and Alon, 2003). Finally, it’s not beyond the realm of
possibility to highlight popular or previously used category options to make
them personalizable too.

Categories

Websites, including the Yahoo! Directory, often present high-level categories to
help users externalize what they are looking for. Several studies (Egan et al., 1989;
Dumais, Cutrell and Chen, 2001) have shown that categorizing results in SUIs
can help users to find results more quickly and more accurately. One key early
system called SuperBook, which automatically created a categorized index over
full-text documents, was shown to help people learn, as measured by quality of
short open-book essays (Egan et al., 1989). More recently, eBay and Amazon
provide searchers with higher level categories so that they can first define what
type of object they are looking for before browsing with richer metadata.

Clusters

One challenge for categories, especially for the whole web, is to categorize all
the data. Another approach, using clustering algorithms in the backend, is to
cluster results by key topics in their content. One early cluster system, called
Scatter/Gather, divided results into clusters of similar topics to highlight the
range of topics covered in a SERP. Evaluation of the Scatter/Gather approach
showed that searchers were easily and quickly able to identify groups of more
relevant documents compared with a standard SERP (Hearst and Pedersen,
1996).

A more recent system, Clusty 6 (Figure 8.11), embodies a clustering method
that creates automatic hierarchical clusters based on the results that are
returned, but is primarily used as a control feature. Despite some studies
showing evidence that clusters help searchers to search (e.g. Turetken and
Sharda, 2005), research has suggested that well designed carefully planned
metadata is better for SUIs than automatically generated annotations (Hearst,
2006a).

Faceted metadata

It has been popular to categorize results in multiple different ways, so that
searchers can express several constraints. Research has shown that, compared
with keyword search, faceted systems can improve search experiences in more
open-ended or subjective tasks (where no single right answer is available)

INTERACTIVE INFORMATION SEEKING, BEHAVIOUR AND RETRIEVAL

150

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

(Stoica and Hearst, 2004). The popular Epicurious website, for example, allows
users to describe recipes that they would like by several types of categories
(called facets), including cuisine, course, ingredient and preparation method.
While the first selection in a facet acts as an input, subsequent selections in facets
act as refinements, and can thus be considered as control.

The Flamenco interface 7 (Yee et al., 2003), shown in Figure 8.12, provides

151

WILSON • INTERFACES FOR INFORMATION RETRIEVAL

Figure 8.11 The Clusty system

Figure 8.12 The Flamenco interface

Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.29085/9781856049740.010
Downloaded from https://www.cambridge.org/core. The University of British Columbia Library, on 04 Feb 2022 at 23:34:23, subject to the Cambridge

INTERACTIVE INFORMATION SEEKING, BEHAVIOUR AND RETRIEVAL

152

several different categories (called facets), which can be used in combination to
define a query. It was used to demonstrate the value of faceted browsing and
represents the standard faceted SUI design. Many variations have been
designed since. Typically, a range of hierarchical or linear facets are provided,
and users can make selections in one or more of them. In Flamenco, used facets
are removed from view, so that remaining facets can receive more attention,
and selections are placed in a breadcrumb list of choices. Removing used facets
provides an effective approach for quickly narrowing results. Other systems
like mSpace8 (schraefel et al., 2006) leave facets in place to encourage exploration
by quickly changing and comparing decisions. mSpace provides an advanced
faceted SUI where the order of facets implies importance and gaps from left to
right are highlighted. Figure 8.13 shows that the two clips in the far right column
are from 1975 and 1974, which would not normally be conveyed in faceted SUIs.
mSpace (and iTunes) facets are only filtered in a left to right direction, and
highlights have been shown to help searchers learn and discover related items
in the remaining unused facets (Wilson, André and schraefel, 2010). Other
systems, including mSpace and eBay, permit multiple selections within single
facets, so, for example, searchers can see results that relate to two price brackets.

Faceted categories are typically used within fixed collections of results, such as
within one website (typically called vertical search), as there must be common
attributes across all the data to categorize them effectively. Although researchers
have tried to apply facets to general web search (Kules, Kustanowitz and
Shneiderman, 2006), Google does not typically provide faceted search, except in
Google Shopping.9 In the nar

Place your order now for a similar assignment and have exceptional work written by one of our experts, guaranteeing you an A result.

Need an Essay Written?

This sample is available to anyone. If you want a unique paper order it from one of our professional writers.

Get help with your academic paper right away

Quality & Timely Delivery

Free Editing & Plagiarism Check

Security, Privacy & Confidentiality