High-level goals, tasks, and strategies

The common starting point of nearly all interaction-process- or phase-models of the information-seeking process is that there is always a user information need at the beginning. This starting situa-tion is often characterized in the IR literature as an anomalous state of knowledge (ASK) [Belkin 1980] / [Belkin, Oddy, Brooks 1982] / [Belkin, Oddy, Brooks 1982a]. Derived from the informa-tion need, the user will have one or more goals explicitly formulated, or implicitly in mind behind his actions. [Hearst 1999] lists “finding a plumber”, “keeping informed about a business competi-tor”, “writing a publishable scholarly article”, and “investigating an allegation of fraud” as exam-ples for goals. Hearst comes from her goals to information access tasks that are used to achieve these goals. These tasks can span from asking specific questions to exhaustively researching a topic. A task example she cites from [O’Day, Jeffries 1993] is “monitoring a well-known topic over time”. This task could, for example, be developed from the goal to be kept informed about a business competitor. From the tasks Hearst comes to a model of interaction, where the information need is the starting point that is to be followed by different steps like “select a system and collec-tion to search on” or “formulate a query”.

Whereas Hearst’s tasks are dependent on the user’s goals, [Goldstein, Roth 1994] developed a model for data exploration where the goals are dependent on the user’s task. However the authors write: “… we classified the types of interactive data exploration tasks (goals) that users will per-form …”. They list for example under data manipulation tasks goals such as “controlling scope” or

“choosing level of detail”. Goals at the same level of detail can also be found in other contexts too, like for example “accurate value lookup” or “comparison of values” in [Roth, Mattis 1990]. This type of goals will be classified here as low-level tasks, and will be discussed later in Chapter 2.2.3 Low-level tasks, goals, and interface actions.

On the same granularity of information access tasks listed by Hearst, [Shneiderman 1998]

differentiates four types of “task actions” listed in Table 2.

Task actions

Specific fact-finding (known-item search) Extended fact-finding

Open-ended browsing Exploration of availability.

Table 2: Task actions according to [Shneiderman 1998]

The two fact-finding tasks both produce clear and replicable outcomes. The main difference be-tween these two types is that in the first case there is a clear stop criterion, when the user finds a document to answer the question. In the second case there is no such clear abort criterion to stop the examination of a result set or the overall search, and therefore the investigation process of a result set or the complete information-seeking process will be much broader in scope and possibly

of longer duration. Even more open and unstructured are the remaining two task actions open-ended browsing and exploration of availability. Trying to fit Hearst’s goal examples in this classi-fication, “finding a plumber” can lead to a specific fact-finding task. Shneiderman’s corresponding example is “Find the telephone number of Bill Clinton”. Hearst’s “keeping informed about a busi-ness competitor” could lead to an extended fact-finding task or open-ended browsing. Here the corresponding examples from Shneiderman are “What genres of music is Sony publishing?” for extended fact-finding and “Is there new work on voice recognition being reported from Japan?”

for open ended browsing. Taking the remaining example goals from Hearst “writing a publishable scholarly article” and “investigating an allegation of fraud” the first task action will probably be an exploration of availability, eventually later followed by more specific task actions. A compari-son of the information access tasks by [Hearst 1999] and the task actions by [Shneiderman 1998]

is shown in Figure 2.

Readily identifable outcome Openess

[Hearst 1999] [Shneiderman 1998]

Asking specific questions Exhaustively researching a topic

Specific fact finding (known item search) Extended fact finding

Open Ended Browsing Exploration of Availabilty

Figure 2: High-level tasks by [Hearst 1999] and [Shneiderman 1998]

[Shneiderman 1998] points out that the task actions are broken down into browsing or searching.

In a next step browsing and searching are represented by interface actions like scrolling or zoom-ing. But before we reach this level of detail two other points should be discussed in more depth:

information-seeking strategies and phases or steps of searching.

Using again the “finding a plumber” example, there are different possibilities to fulfill the informa-tion need. [Baeza-Yates, Ribeiro-Neto 1999] emphasize, when using a retrieval system for ASK-situations, the distinction between two different types of strategies: information or data retrieval on the one hand and browsing on the other. In fact, they categorize retrieval and browsing as two dif-ferent types of tasks. The general distinction between searching (sometimes also named direct querying or retrieval by specification) and browsing (sometime also named scanning or retrieval by recognition) is very common in the literature. As shown above, Shneiderman makes the same distinction, however not directly using the term “task” on this level. Because the term task is used in such an inflationary way by many authors, it seems to be more appropriate to classify these dif-ferent types of behavior as strategies like for example done by [Henninger, Belkin 1996]. Having a closer look at information-seeking strategies [Belkin, Marchetti, Cool 1993] and [Belkin, Cool, Stein et al. 1995] try to structure the field by defining a multi-dimensional space of information-seeking strategies. For this purpose they use four dimensions: method of interaction (scanning searching), mode of retrieval (recognition specification), goal of interaction (learning select-ing), and resource considered (information meta information). With these dimensions they cre-ate a matrix that shows the possible combinations in the form of sixteen different Information-Seeking Strategies (ISS). Table 3 shows a selection of the most interesting ISSs in the context of this thesis.

ISS Method of Interaction Mode of Retrieval Goal of Interaction Resource Considered

ISS5 Scan Recognize Select Information

ISS7 Scan Specify Select Information

ISS13 Search Recognize Select Information

ISS15 Search Specify Select Information

Table 3: Examples of Information-Seeking Strategies ISS according to [Belkin, Marchetti, Cool 1993] and [Belkin, Cool, Stein et al. 1995]

The goal of interaction as a dimension of the matrix created by Belkin et al. focuses on the re-trieval system used. The two modes are “learn” and “select”. For the resource considered, the dis-tinction between “information” and “meta information” is a classical IR category. The subtle dif-ferentiation between method of interaction and mode of retrieval is particularly interesting. The authors point out that scanning is typically associated with retrieval by recognition, and searching with retrieval by specifications, but they present examples where this typical connection is broken up. Another important point Belkin et al. emphasize is possible changes of the ISS during an in-formation-seeking episode. Depending on previous knowledge, the user will start an information-seeking process with a certain strategy. Getting the first results may cause him to change this strat-egy. The next set of results may cause another change and so on. The idea that information seeking is not always a straightforward process with one best strategy can also be found in other models.

One of the most famous ones, which also emphasizes the diversity of strategies, is the berrypicking model of [Bates 1989]. She also points out that it is not only the strategy that may change, but also the information need itself. Another important message from Bates is that the information need may not be satisfied by a single, final retrieved set of documents. All or part of the information chunks found on the way may also contribute to satisfying the information need(s). Bates lists six widely used information-seeking strategies: footnote chasing or backward chaining, citation searching or forward chaining, journal run, area scanning, subject search in bibliographies and abstracting and indexing services, and author searching. These strategies as parts of the berrypick-ing model were observed when people used manual sources. At the end of the 1980s, Bates had great expectations that hypertext approaches would be ideal for berrypicking. What was true for hypertext will also be true for the Web as the biggest hypertext so far formed.

The findings of Bates are supported by a number of authors like [O’Day, Jeffries 1993] or [Hearst 1999]. The former studied the use of information search results by fifteen regular clients of profes-sional intermediaries. As shown above, Web searching is mainly end-user-searching. Nevertheless, the patterns they found for mediated searches may also occur in Internet searching. They classified three basic search modes: monitoring, planned, and exploratory. Or in more detail: monitoring a well-known topic or set of variables over time, following an information-gathering plan suggested by a typical approach to the task at hand, and exploring a topic in an undirected fashion. In addi-tion they identified patterns of interconnected searches. They established that the accumulaaddi-tion of search results had value for the end-users - not only the final result set – and this even for mediated searches. It may be even more the case for end-user searching.

Focusing back on the internet [Baeza-Yates, Ribeiro-Neto 1999] expand their above listed two different tasks retrieval and browsing to three basic forms of searching for information in the Web:

the use of search engines, that index a portion of the Web documents as a full-text database, the use of Web directories, which classify selected Web documents by subject, and the exploitation of the hyperlink structure of the Web for search purposes. In fact we have three different strategies

here where the use of search engines corresponds strongly to the classical search strategy, and the two other ones are both varied forms of classical browsing.

Also appealing is an approach from [Choo, Detlor, Turnbull 1998] / [Choo, Detlor, Turnbull 1999]

combining Aguilars’s four modes of organizational scanning [Aguilar 1967] with the six catego-ries of information seeking behavior defined by [Ellis 1989], to a new model of modes and moves for information seeking in the Web. The modes are: undirected viewing, conditioned viewing, informal search, and formal search. For every mode they attach a number of moves (information seeking categories) shown as categories in Table 4. The authors verified the model by analyzing 61 Web information seeking episodes of 34 Web users from different professions. The strength of the model is its clear and simple structure; however, its main weakness is that not all of the real-world possibilities can be adequately placed in a cell of the model. Chaining, for example, is only attached to undirected viewing, but can surely also sometimes be found in formal search mode (even when not found in this combination in the 61 episodes). What is in any case interesting is their comparison of literature search moves from [Ellis 1989] with their Web moves equivalents shown in Table 4.

Category Literature Search Moves Anticipated Web Moves

Starting Identifying sources of interest Identifying websites/pages containing or pointing to information of interest

Chaining Following up references found in given material

Following links on starting pages to other content-related sites

Browsing Scanning tables of contents or headings Scanning top-level pages: lists, headings, site maps Differentiating Assessing or restricting information

accord-ing to their usefulness

Selecting useful pages and sites by bookmarking, print-ing, copying and pastprint-ing, etc.

Choosing differentiated, pre-selected site Monitoring Receiving regular reports or summaries

from selected sources

Receiving site updates using e.g. push, agents, or pro-files

Revisiting ‘favorite’ sites Extracting Systematically working a source to identify

material of interest

Systematically searches a local site to extract informa-tion of interest at that site

Table 4: Comparison of literature search and Web moves according to [Choo, Detlor, Turnbull 1999] Fig. 2.

Other models in the area of Web information seeking try to cope with special artifacts of the proc-ess. An example for this is the work of [Navarro-Prieto, Scaife, Rogers 1999]. After a study per-formed with 10 Computer Science and 13 Psychology students, they defined different Web search models for users with high and low experience to make predictions about the participants’

searches. The model for experienced searches is much more complex than the one for novices.

As we have seen there are a number of different high-level models available which look at how to structure the information-seeking process in the form of goals, tasks or strategies. More detailed overviews and discussions can be found in [Hearst 1999] or [Morse 1999]. For the context used here the following four most important conclusions can be drawn out of the different approaches:

• Classical search is just one of the possible ways to fulfill an information need

• Goals and strategies are not static, but may change during an information-seeking episode

• Not only the final result set is important, a number of factors contributing to fulfilling the information need may also come along the way

• Strategies may depend on user experience

Shneiderman’s task action model [Shneiderman 1998] shown in Table 2 on page 20 will be fo-cused on as a concrete task model in the remainder of this thesis. The content area of this thesis is the visualization of search results; therefore the next chapter, discussing lower levels of abstrac-tion, will concentrate mainly on the aspects of searching as a strategy, despite the fact that there are a number of other possibilities which can be used in fulfilling an information need.