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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

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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

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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.

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

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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’.

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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

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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.

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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.

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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

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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.

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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.

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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.

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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

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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.

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(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

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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)

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(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

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Figure 8.11 The Clusty system

Figure 8.12 The Flamenco interface

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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 narrower space of searching for products, there are common factors like price and shop that can be easily applied to all of the results.

Figure 8.13 T

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