This chapter describes interfaces to support navigation (or browsing) as part of the search process. Chapter 3 discusses theoretical models of browsing versus search as information seeking strategies, as well as the notion of information scent and information architecture. As mentioned there, one way to distinguish searching versus browsing is to note that search queries tend to produce new, ad hoc collections of information that have not been gathered together before, whereas navigation/browsing refers to selecting links or categories that produce pre-defined groups of information items. Browsing activities can also include following a chain of links, switching from one view to another, in a sequence of scan and select operations. Browsing can also refer to the casual, mainly undirected exploration of navigation structures.

Navigation structures lend themselves more successfully to books, information collections, personal information, Web sites, and retrieval results than to vast collections such as the Web. Nonetheless, there have also been attempts to organize very large information collections such as the Web.

Category systems are the main tool for navigating information structures and organizing search results. A category system is a set of meaningful labels organized in such a way as to reflect the concepts relevant to a domain. A fixed category structure helps define the information space, organizing information into a familiar structure for those who know the field, and providing a novice with scaffolding to help begin to understand the domain. Category system structure in search interfaces is usually either flat, hierarchical, or faceted (this is discussed in detail below).

This chapter describes several areas that are gaining in importance and are likely to play a significant role in search interfaces in the future. These predictions come primarily from extrapolating out from emerging trends. The areas that seem most likely to be important, and so demand changes in search interface technology, are: mobile search, multimedia search, social search, and a hybrid of natural language and command-based queries in search. Each is discussed in detail below.

Mobile Search Interfaces

Mobile communication devices are becoming increasingly popular as information access tools. A recent survey of more than 1,000 technology leaders and other stakeholders said they expect that mobile devices will be the primary connection tool to the Internet for most people in the world in 2020 (Rainie 2008). They also predict that voice recognition and touch user interfaces will be more prevalent and accepted by 2020. Usable interfaces for search on mobile devices are only now beginning to emerge, but this area will most certainly undergo rapid development and innovation in the next few years.

Until recently, mobile devices had small screens, awkward text input devices, and relatively low bandwidth, which pose challenges for information-rich applications like search. However, bandwidth is increasing, and the recent appearance of personal digital assistants (PDAs) with relatively large, high-resolution screens, such as the Apple iPhone, are making search on mobile platforms feel more like desktop-based Web search.

Keeping the Interface Simple

The job of the search user interface is to aid users in the expression of their information needs, in the formulation of their queries, in the understanding of their search results, and in keeping track of the progress of their information seeking efforts.

However, the typical search interface today is of the form type keywords- in-entry-form, view-results-in-a-vertical-list. A comparison of a search results page from Google in 2007 to that of Infoseek in 1997 shows that they are nearly identical (see Figure 1.1). Why is the standard interface so simple? Some important reasons for the relative simplicity and unchanging nature of the standard Web search interface are:

• Search is a means towards some other end, rather than a goal in itself. When a person is looking for information, they are usually engaged in some larger task, and do not want their flow of thought interrupted by an intrusive interface.

• Related to the first point, search is a mentally intensive task. When a person reads text, they are focused on that task; it is not possible to read and to think about something else at the same time. Thus, the fewer distractions while reading, the more usable the interface.

• Since nearly everyone who uses the Web uses search, the interface design must be understandable and appealing to a wide variety of users of all ages, cultures and backgrounds, applied to an enormous variety of information needs.

The preceding chapters have discussed user interfaces to support search, with a focus on what is known to be successful (from a usability perspective) for the vast majority of searchers. This and the following chapter describe efforts to improve search interfaces by incorporating visual information into the display using techniques from the field of information visualization.

The human perceptual system is highly attuned to images, and visual representations can communicate some kinds of information more rapidly and effectively than text. For example, the familiar bar chart or line graph can be much more evocative of the underlying data than the corresponding table of numbers (Larkin and Simon 1987). The goal of information visualization is to translate abstract information into a visual form that provides new insight about that information. Visualization has been shown to be successful at providing insight about data for a wide range of tasks.

The field of information visualization is a vibrant one, with hundreds of innovative ideas burgeoning on the Web. However, applying visualization to textual information is quite challenging, especially when the goal is to improve search over text collections. As discussed in earlier chapters, search is a means towards some other end, rather than a goal in itself. When reading text, one is focused on that task; it is not possible to read and visually perceive something else at the same time. Furthermore, the nature of text makes it difficult to convert it to a visual analog. Most likely for these reasons, applications of visualization to general search have not been widely accepted to date, and few usability results are positive.

This chapter describes interfaces for the search results presentation portion of the information seeking process, focusing for the most part on ideas that are currently in use.

Document Surrogates

The most common way that search results are displayed is as a vertical list of information summarizing the retrieved documents. (These search results listings are often known as “search engine results pages,” or SERPs, in industry.) Typically, an item in the results list consists of the document's title and a set of important metadata, such as date, author, source (URL), and length of the article, along with a brief summary of a relevant portion of the document (see Figure 5.1). The representation for a document within a results listing is often called a search hit. This collection of information is sometimes referred to as the document surrogate. Marchionini and White (2008) note that document surrogates are summary information intended to help the user understand the primary object, as opposed to metadata more broadly construed, which can also serve this purpose but is often more tailored towards use by computer programs.

The quality of the document surrogate has a strong effect on the ability of the searcher to judge the relevance of the document. Even the most relevant document is unlikely to be selected if the title is uninformative or misleading. (Some Web search algorithms try to capture the quality of the title description as part of the ranking score.) The descriptiveness of the summary is also very important and is discussed in detail below.

As discussed in the previous chapter, visualization when applied to text seems to be most effective for specialists doing data analysis. Although this is an exciting field, it is not what most people think of when one talks about search interfaces. Unfortunately, some researchers working on visualization of text conflate search tasks with data analysis tasks. For example, Veerasamy and Heikes (1997) critique one interface for making it “more difficult than in our tool to gain an overall picture of the query word distribution for a whole set of documents in one glance.” It is unclear why a searcher would want to see such a distribution, even though such a view may be of great interest to a computational linguist.

This chapter describes ideas that have been put forward for understanding the contents of text collections from a more analytical point of view. The first section discusses applications in the field of Text Mining, which usually involve visualizing connections among entities within and across documents. The next section discusses methods for visualization occurrences of words or phrases within documents, in what have classically been called concordances. The final section discusses various attempts to visualize relationships between words in their usage in language and in lexical ontologies.

Visualization for Text Mining

There is great interest in the field of text mining, which can be defined as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources (Feldman and Sanger 2006; Hearst 1999a).

Currently most Web search engines produce the same results independently of who the user is; search engines are in fact designed to do well at meeting the average goals of the group over the specific goals of the individual (Teevan et al. 2005a). Nonetheless, researchers, marketers, and search pundits alike often state that search results should reflect the fact that people differ in their needs, goals, and preferences (Pitkow et al. 2002). As a typical example, an avionics expert should see a different results ranking for a query on jets than a football fan from New York.

Tailoring search results to an individual's interests is just one form of information personalization. The term is currently applied to a diverse array of information applications. Given a profile of a user's interests, information content can be personalized by: re-ranking search results listings to better reflect the user's preferences, automatically notifying a user when new articles of interest appear or important changes are made to existing documents, recommending items that are related in some way to an item currently being viewed, and customizing the look and content of a Web site or other information artifact.

Information personalization approaches vary along several dimensions. Some make use of information that users explicitly specify about themselves and their interests, while others attempt to infer users' preferences implicitly from their actions. Some approaches note that individuals tend to have both long-term, stable interests, and short-term temporary interests, and that these can come into conflict when proposing recommendations based on past actions.

Chapter 3 discussed the various theories associated with the information seeking process, and the subsequent chapters described interfaces for supporting query specification, results presentation, and query reformulation – the standard stages of the search process. This chapter describes interface ideas for other aspects of the search process: search starting points, search history, and interfaces that support the process as a whole. The final section discusses attempts to integrate search into the sensemaking process.

Starting Points for Search

The first step in addressing an information need is deciding which tools to use and which collections to search over, a process which is sometimes referred to as source selection. Today there are many choices, including phoning, emailing, or texting a friend, reaching for a physical book, going to a physical library, or sitting down at a networked computer and starting up a Web browser.

Starting Points in Web Search

For those who go online, today the most common starting point is to open a Web browser and start with a Web search engine. Today, Web browsers make that choice even easier by including an always-visible entry form in the browser's “chrome” or by supporting search directly in what used to be the address entry form. But people also commonly start searches from favorite information resources, such as bookmarked Web sites (Teevan et al. 2004). Web browsers have always allowed users to retain bookmarks, but today are making site revisitation even easier by showing usage history as a drop-down menu within the address bar, and matching that history as the user types.

In order to design successful search user interfaces, it is necessary to understand the human information seeking process, including the strategies people employ when engaged in search. Numerous theoretical treatments have been proposed to characterize this complex cognitive process (Belkin et al. 1982; Jarvelin and Ingwersen 2004; Kuhlthau 1991; Marchionini 1995; Saracevic 1997; Sutcliffe and Ennis's 1998). This chapter presents the most commonly discussed theoretical models of the search process: the standard model, the cognitive model, the dynamic model, search as a sequence of stages, search as a strategic process, and sensemaking. The chapter concludes with a discussion of information needs, including methods for inferring information needs from their expression as queries.

The Standard Model of Information Seeking

Many accounts of the information seeking process assume an interaction cycle consisting of identifying an information need, followed by the activities of query specification, examination of retrieval results, and if needed, reformulation of the query, repeating the cycle until a satisfactory result set is found (Salton 1989; Shneiderman et al. 1998). As Marchionini (1989) puts it:

This model is elaborated by Sutcliffe and Ennis's (1998) oft-cited information seeking process model, which they formulate as a cycle consisting of four main activities:

• Problem identification,

• Articulation of information need(s),

• Query formulation, and

• Results evaluation.