USER BEHAVIOR UNDER THE MICROSCOPE

Can the Behavior Indicate a Web User’s Task?

Anne Gutschmidt and Clemens H. Cap

Computer Science Department, Albert-Einstein-Str. 21, 18059 Rostock, Germany

Keywords:

User tasks, user behavior, exploratory study, automatic user task identiﬁcation.

Abstract:

The automatic recognition of a user’s current task by the surﬁng behavior requires detailed knowledge of the

relationship between task and behavior. An exploratory study was conducted where 20 participants performed

exercises on a given Web site. These exercises corresponded to the predeﬁned user tasks Fact Finding, Infor-

mation Gathering and Just Browsing following present research on user activities. The resulting behavior was

recorded in detailed event log ﬁles which contain every action performed in the browser, such as mouse moves

and clicks, scrolling, the use of the back button etc. The analysis of variance indicates that the three tasks can

be differentiated with a combination of selected behavioral attributes.

1 INTRODUCTION

Recognizing the user’s current task only by the

behavior is an ambitious objective. If a user’s

task was known, various ways of support could be

achieved. Present personalization mechanisms, e.g.

recommender systems such as Amazon’s, work on the

basis of long term user behavior and information ex-

plicitly given, like product evaluations and purchases

(Linden et al., 2003). They do not take into consider-

ation the user’s current needs, e.g. if the user is look-

ing something up quickly or just searching for enter-

tainment, or if the person intends to collect informa-

tion about a topic. This knowledge could improve the

quality of many personalization mechanisms such as

link suggestions.

The problem is that not every little bit of the be-

havior, but only a selection of behavioral attributes, is

crucial in identifying the user’s task. An exploratory

study was conducted to determine which attributes

may be suitable for task recognition. Two aspects of

the study must be particularly emphasized:

First, the task is always known in the experiment

and, thus, represents the independent variable. The

behavior, in contrast, is considered dependent on the

task. This way, the participants’ intentions are always

known and the relationship between task and behavior

can be scrutinized. In later practice, the direction will

naturally be reverse with the user task being deduced

from the behavior.

Second, the behavior is recorded in form of a de-

tailed log containing every action processed in the

Web browser, like mouse clicks, scroll moves, the us-

age of single pages, tabs and even browser buttons

and menus. This allows various evaluations on the

data which go beyond plain click stream analyses, like

(Das et al., 2007) and (G

ery and Haddad, 2003).

The procedure of the study is based on three user

tasks following recent publications in this ﬁeld such

as (Kellar et al., 2006): Fact Finding, Information

Gathering and Just Browsing. First evaluations on the

collected data indicate that there exist signiﬁcant dif-

ferences between these tasks with regard to the overall

duration of a task, the average time a user was look-

ing at a page and the number of pages the user has

looked at per minute. As the experiment was con-

ducted on the Web site of an on-line newspaper, also

speciﬁc attributes of the behavior, like the number of

news categories visited, were investigated, which also

revealed signiﬁcant differences between the tasks.

The paper is structured as follows: In the next

section the state of the art concerning user task tax-

onomies and task recognition is presented. After-

wards, the exploratory study is described followed by

ﬁrst results of the analysis of variance and t-tests. At

the end a summary is given.

215

Gutschmidt A. and H. Cap C. (2008).

USER BEHAVIOR UNDER THE MICROSCOPE - Can the Behavior Indicate a Web User’s Task?.

In Proceedings of the Fourth International Conference on Web Information Systems and Technologies, pages 215-222

DOI: 10.5220/0001526202150222

 SciTePress

2 USER TASKS AND THEIR

RECOGNITION

The ﬁrst step towards user task identiﬁcation is the

deﬁnition of user tasks and bringing them into a tax-

onomy. Then it is possible to look for ways of recog-

nizing the tasks.

In the ﬁeld of discovering user needs, several

branches have evolved. One branch deals with Web

search, where search engine queries are assigned to

certain goals which can be interpreted as user tasks.

(Broder, 2002) formulated three groups of informa-

tion needs in the context of Web queries: naviga-

tional (searching for a particular Web site), informa-

tional (searching for information about a topic, one

site is not sufﬁcient) and transactional (ﬁnding sites

that enable transactions like shopping or downloads).

(Rose and Levinson, 2004) arrived at a similar tax-

onomy, only transactional was replaced by a more

general task, resource, comprising the subtasks ob-

tain, download, entertain and interact. Both (Broder,

2002) and (Rose and Levinson, 2004) do not yet pro-

vide an approach to automatic task identiﬁcation, but

reinforce their taxonomies by manual classiﬁcations.

An approach to identifying tasks is to be found in

(Lee et al., 2005), where only the matching tasks of

(Broder, 2002) and (Rose and Levinson, 2004), nav-

igational and informational, are considered. Using

the features click distribution and anchor-link distri-

bution, good classiﬁcation results were achieved with

a rate of 80% or more correct classiﬁcations, and 90%

when both features were used in combination.

Another branch regarding user tasks deals with

general user activities on the Web. An early sug-

gestion for a user task taxonomy was introduced in

(Byrne et al., 1999). The activities they describe be-

long to a rather low level of abstraction, i.e. they de-

ﬁne tasks like Locate (searching for something on a

Web page, like a word or a picture), Go To (look-

ing for a certain Web page), or Conﬁgure (changing

the state of the browser, e.g. through scrolling). More

suggestions of user task taxonomies at higher levels of

abstraction followed, like (Choo et al., 2000; Morri-

son et al., 2001; Sellen et al., 2002). (Kellar et al.,

2006) merged these three approaches and included

ﬁndings from their own studies. The resulting taxo-

nomy is described as follows:

• Fact Finding: The users have an exact target in

mind, they are looking for a keyword, a date or a

sentence. Activities belonging to this category are

usually of short duration.

• Information Gathering: The users are searching

for information on a topic, thus, the target is more

open, while still being restricted by the topic. In-

formation Gathering usually takes more time and

need not be completed within one session.

• Just Browsing: The users surf through the Inter-

net as they like and with no particular target in

mind. Activities like these may also be of longer

duration.

• Transaction: The users want to perform trans-

actions like on-line banking or checking their e-

mails on-line.

• Other: This category comprises all activities

which cannot be assigned to any of the other four

categories.

Based on this taxonomy, an attempt of automatic

task identiﬁcation was made using classiﬁcation. In

a study, (Kellar et al., 2006) collected log data con-

taining events like the usage of the back, forward and

reload buttons, as well as of bookmarks, hyperlinks,

and the history. One part of the data sets was classi-

ﬁed and served as training data to classify the remain-

ing data sets. On the whole, only 53.8% of the data

sets were classiﬁed correctly. The authors see the rea-

son for these results in the strong impact individual

differences have on the surﬁng behavior (Kellar and

Watters, 2006). However, there may be further rea-

sons: First, differences between Web sites concerning

structure, layout and content certainly have an impact

on the way users surf. Second, the authors considered

only low-level events which probably do not describe

the task-dependent behavior in an appropriate way.

3 THE STUDY

3.1 Set-up

20 university students and employees from different

departments took part in the study. Their average age

was 26.6 years.

The experiment was conducted with each partici-

pant separately, with an investigator leading through

the procedure. The participants had to surf on one

version of a German on-line newspaper, Spiegel On-

line.

Exercises were set, where each exercise repre-

sented either Fact Finding, Information Gathering or

Just Browsing following the taxonomy of user tasks

suggested in (Kellar et al., 2006). However, the cat-

egory Transaction was not adapted as activities like

these were not likely to occur in the study. It was de-

cided to restrict the investigations to this Web site in

http://www.spiegel.de

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216

order to eliminate the impact other Web sites of dif-

ferent structure and content might have on the users’

behavior. Spiegel Online is structured like many other

on-line newspapers with a start page containing the

latest headlines and links to the different news cate-

gories like politics or economics. For each news cat-

egory, extra index pages are offered, the articles each

span one or more pages.

While the participants were working on the

exercises, each of their actions within the browser

was captured. At the end of the experiment a ﬁle

containing all these actions in chronological order

was created. Finally, the participants had to answer a

few questions about their usage of the computer, the

Internet and on-line newspapers as well as about their

browser preferences.

3.2 Exercises

The participants had to go through four exercises

which corresponded to the user tasks Fact Finding,

Information Gathering and Just Browsing:

Exercise 1: The participants were asked to get

familiar with the pages and to browse the Web site

as they liked. The only restriction was to remain on

Spiegel Online. This warm-up phase was meant to

represent Just Browsing. No explicit time limit was

given, but the investigator interrupted the activities

after ten minutes if necessary.

Exercise 2: The participants had to look for a

weather forecast of a certain town for the next day

which corresponded to Fact Finding. The exercise

ended as soon as the target was reached.

Exercise 3: Another Fact Finding exercise was set

which was to ﬁnd a certain football result.

Exercise 4: The test was concluded with an Informa-

tion Gathering exercise. The participants were asked

to collect information on the G8 summit. As the

newspaper version used for the experiment was from

June 8, 2007, the actual date of the summit, many ar-

ticles about the topic were offered on the Web site

at that time. To motivate the participants they were

informed that they would have to answer a few ques-

tions about the topic afterwards. A time limit of ten

minutes was given.

3.3 Data Collection

A Mozilla Firefox extension was used to capture

events concerning mouse, scrolling, opening and

closing pages and tabs, keyboard and the browser.

Beside mouse clicks, every move of the mouse was

recorded as well as the contact with page elements

like hyperlinks, pictures or headlines. The scroll

record also contained information on how much of the

page was above and below the current view. More-

over, page events, the appearance and disappearance

of pages, were captured. These events need not cor-

respond to the appearance and disappearance into the

user’s sight. Sometimes, users like to load pages in

tabs in the background, so that these pages are not

seen until the tab is selected. That is why tab events

are recorded as well. Furthermore, the usage of the

keyboard is contained in the log. Browser events com-

prise the usage of browser buttons, like “back”, “for-

ward” or “reload”, as well as the usage of all browser

menus.

For each event additional information is saved,

e.g. in the case of a mouse click a description of the

element that was clicked is stored.

3.4 Data Analysis

First comparisons of the three user tasks Fact Finding,

Information Gathering and Just Browsing will be pre-

sented concerning the following behavioral attributes:

• The duration of the exercise which corresponded

to the user tasks

• The average duration of a page view

• The number of page views per minute

• The time spent on the newspaper’s start page in

proportion to the overall duration of the exercise

• The number of different news categories visited

A page view refers to the period of time a user is

looking at a Web page. Pop-up windows and the use

of different tabs are included in this concept; e.g. a

pop-up window ends the page view of the page behind

and starts a new page view for the pop-up window,

whereas loading tabs in the background does not start

a page view, but the selection of a tab does. The page

views were derived from the log together with their

duration and the URL.

The question is between which of the three tasks

signiﬁcant differences exist considering each of the

ﬁve behavioral attributes. Analyses of variance with

repeated measures are applied to ﬁnd out if such sig-

niﬁcant differences are present. If this is the case, the

outcome will be a p-value smaller than 0.05. To reveal

USER BEHAVIOR UNDER THE MICROSCOPE - Can the Behavior Indicate a Web User’s Task?

217

where the actual differences lie, t-tests with paired

samples are used. Usually, the alpha level is also 0.05,

but as there are more than two groups to be compared,

the Bonferroni correction is applied, which is 1/3α

(0.0167) in this case, to avoid the alpha error cumula-

tion. This correction is applied to all t-tests and leads

to more conservative results. A signiﬁcant difference

is a hint which behavioral attribute to concentrate on

for an automatic recognition of user tasks.

For the following evaluations, the respective val-

ues of exercise 2 and 3, both belonging to Fact Find-

ing, were merged by taking the average values to ob-

tain one single group for this task.

4 RESULTS

In this section, the results of the analysis of variance

and the t-tests concerning the ﬁve above-mentioned

behavioral attributes are presented. Moreover, des-

criptive data in the form of average values and stan-

dard deviations are given. Due to space constraints

only some frequency distributions are presented.

4.1 Task Duration

Figure 1 illustrates the average values of the task du-

ration, the time a participant worked on an exercise,

with the concrete values at the bottom of each bar.

The bars are furthermore enhanced with an error bar

depicting the standard deviation. The error bar is

weighted to the top and to the bottom by the propor-

tion of the sum of the squared deviations from the av-

erage value. As can be seen in Figure 1, the overall

duration of the exercises for Fact Finding is shorter

than for the other two tasks. Information Gathering

and Just Browsing seemed to require clearly more

time. This impression is conﬁrmed by the results

of the analysis of variance with repeated measures

with a signiﬁcance indicated by p < 0.001 (df = 2,

F = 56,405). T-tests with paired samples with an

alpha level of α = 0.0167 reveal signiﬁcant differ-

ences between Fact Finding and Information Gath-

ering (p < 0.001, df = 19, T = −10.963) and be-

tween Just Browsing and Fact Finding (p < 0.001,

df = 19, T = 9.681). A signiﬁcant difference between

Just Browsing and Information Gathering is not given

(p = 0.315, df = 19, T = −1.033).

These results match with the picture of the fre-

quency distributions in Figure 2. The Figure also

shows that the frequency distribution for Fact Finding

already comes close to normal distribution whereas

the other two tasks seem to be uniformly distributed.

Figure 1: Overall duration of the exercises depending on the

user task, σ

= 0.27, σ

= 2.6, σ

= 2.5.

Figure 2: The frequency distributions for the attribute task

duration.

Regarding the low number of values, however, this

cannot be considered as certain.

4.2 Average Page View Duration

Looking at Figure 3, Information Gathering is on top

concerning the average duration a user spends on a

Web page. Just Browsing shows a smaller average

page view duration, but the latter is still greater than

the time Fact Finders spend on a single page on av-

erage. The analysis of variance indicates signiﬁcant

differences between the three user tasks (p < 0.001,

df = 2, F = 11,37). According to the t-tests, these

differences lie between Fact Finding and Informa-

tion Gathering (p = 0.001, df = 19, T = −3.729)

and between Just Browsing and Fact Finding (p =

0.005, df = 19, T = 3.143). With regard to the aver-

age page view duration, Just Browsing and Informa-

tion Gathering do not seem to be signiﬁcantly differ-

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Figure 3: Average page view duration depending on the user

task, σ

= 0.1, σ

= 0.7, σ

= 0.5.

ent (p = 0.026, df = 19, T = −2.414).

The high values of the standard deviations in the

groups Just Browsing and Information Gathering are

remarkable. A reason for this result may be that the

participants were differently motivated when taking

part in the experiment. Another reason could be gen-

eral individual differences in the surﬁng behavior. If

further tests, however, conﬁrm strong differences be-

tween the individual users, this might turn out to be a

problem for the identiﬁcation of the user task by this

attribute.

4.3 Number of Page Views Per Minute

Figure 4 indicates that Fact Finders seem to look at

more pages during one minute than it is the case in the

other two groups. This seems self-evident, because

it is probable that users skim rather than read when

looking for a fact. In contrast, the participants seemed

to read more carefully when doing Just Browsing and

Information Gathering. The analysis of variance re-

sults in a p-value p < 0.001 (df = 2, F = 28.349).

The t-tests show signiﬁcant differences between Fact

Finding and Information Gathering (p < 0.001,

df = 19, T = 7) and between Just Browsing and

Fact Finding (p < 0.001, df = 19, T = −5.293).

There is no signiﬁcant difference between Just Brows-

ing and Information Gathering (p = 0.111, d f = 19,

T = 1.673).

Figure 5 shows the frequency distributions for all

three tasks. As can be seen, the curves indicate nor-

mal distributions.

Figure 4: The number of page views per minute depending

on the user task, σ

= 1.1, σ

= 0.9, σ

= 1.2.

Figure 5: The frequency distributions for the attribute page

views per minute.

4.4 Time Spent on the Start Page

The behavioral attributes considered so far are quite

general and can be referred to other Web sites as

well. The time spent on the start page compared to

the overall duration, however, is an attribute very spe-

ciﬁc to on-line newspapers. As can be seen in Fig-

ure 6, a clear difference shows between Information

Gathering and Just Browsing. It was common with

Just Browsing as well as with Information Gather-

ing that the participants had a few pages which con-

tained a set of useful links and to which they fre-

quently went back to try further interesting links. In

the case of Just Browsing, the start page was such

an important page, whereas pages more specialized

to the given topic were used for Information Gather-

ing. The outcome of the analysis of variance is a p-

value of p = 0.008 (df = 2, F = 5.446), the concrete

signiﬁcant differences lie, according to the t-tests,

USER BEHAVIOR UNDER THE MICROSCOPE - Can the Behavior Indicate a Web User’s Task?

219

Figure 6: The time proportion of start page visits depending

on the user task, σ

= 14%, σ

= 19%, σ

= 24%.

between Fact Finding and Information Gathering

(p = 0.014, df = 19, T = 2.711) and between Just

Browsing and Information Gathering (p = 0.007,

df = 19, T = 3,051). This time, there is no signiﬁcant

difference between Just Browsing and Fact Finding

(p = 0.802, df = 19, T = 0.255). Again, the standard

deviations are very high and have to be scrutinized in

further tests.

4.5 Number of News Categories

Another behavioral attribute which is speciﬁc to on-

line newspapers is the number of news categories

the users are visiting to reach their goal. The news

categories correspond to the categories presented in

the navigation menu of the considered newspaper,

such as politics, economics, sports etc. Figure 7 in-

dicates differences between all three tasks which is

conﬁrmed by the analysis of variance (p < 0.001,

df = 2, F = 32.351) and the t-tests revealing signiﬁ-

cant differences between Fact Finding and Informa-

tion Gathering (p < 0.001, df = 19, T = −6,020),

Just Browsing and Fact Finding (p < 0.001, d f =

19, T = 6.322) as well as between Just Browsing

and Information Gathering (p < 0.001, df = 19,

T = 4.714). In contrast to Information Gathering and

Fact Finding, Just Browsing meant that the partici-

pants were interested in several different news cat-

egories as they did not have a concrete target and

surfed as they wished. Fact Finding was restricted to a

speciﬁc service, the weather forecast, which does not

belong to a news category, and one category, sports.

That is why an average value of 0.5 occurs. The In-

formation Gathering task could have led to different

news categories like politics, economics and culture,

the participants, however, mostly surfed on only one

Figure 7: The number of news categories visited depending

on the user task, σ

= 0.1, σ

= 0.6, σ

= 1.8.

or two categories.

Due to high standard deviations and a lack of a

greater number of realistical Fact Finding and Infor-

mation Gathering examples further tests should be

made to conﬁrm the results of the t-tests. However,

the tendencies revealed here seem promising for the

testing of hypotheses like “Just Browsing involves

more news categories than the other tasks.”

4.6 Conclusions

The analyses of variance and the t-tests indicate

that Fact Finding, Information Gathering as well as

Just Browsing can be distinguished using the above-

mentioned behavioral attributes. It depends on the ob-

served attribute, however, between which tasks signif-

icant differences can be found.

Often, high standard deviations occurred. Their

origin cannot yet be explained: either the participants

were differently motivated in the experiment or their

individual way of surﬁng is too different or there is

a completely different reason. Further investigations

with higher samples will hopefully bring more insight

into this problem. Individual differences in the behav-

ior might make the respective attribute impossible to

use for the automatic identiﬁcation of user tasks. On

the other hand, it is interesting to know which behav-

ioral aspects are more than others inﬂuenced by the

individual.

With regard to three of ﬁve attributes, Fact Find-

ing was always best to differentiate from the other

two user tasks. The differences between Informa-

tion Gathering and Just Browsing were not signiﬁ-

cant with these three attributes, although the differ-

ence concerning the average page view duration ap-

pears promising (p = 0.026).

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220

The attribute describing the time spent on the

start page in proportion to the overall duration indi-

cated a difference between Information Gathering and

Just Browsing which supplements the other three at-

tributes. The number of news categories even showed

signiﬁcant differences between all three user tasks.

Until now, the identiﬁcation of user tasks seems to

depend on attributes which are connected to the type

of Web site; e.g. the number of news categories vis-

ited. It will be interesting to ﬁnd out if this is still

the case when more aspects of the behavior will have

been included.

Up to now, the potential of the collected data

has not yet been exhausted as this is still a work in

progress. Further analyses are necessary. They will

be devoted to aspects like the scroll behavior; e.g.

maybe slow scrolling to the bottom of the page in-

dicates careful reading which might belong to Infor-

mation Gathering. Another aspect is the touching of

page elements, like pictures and titles. It is known

that the position of the mouse cursor is often near the

spot a user is looking at (Chen et al., 2001). More-

over the complexity of the page content, e.g. reﬂected

in the amount of text, should be included in the anal-

ysis of page view durations. Page revisitations may

also play an important role and should be considered

as well. The list of analyses planned for the future is

obviously long.

Based on the ﬁndings presented here and those

further detailed analyses, hypotheses will be formu-

lated which must be tested in a ﬁeld study. This study

will have to be conducted in a more natural setting;

i.e. the recording tool has to be installed on the partic-

ipants’ computers and watch them in everyday activi-

ties with no exercises being set. The hypotheses will

be like “Fact Finding shows shorter page views than

Just Browsing”, “Fact Finding shows shorter page

views than Information Gathering” and “Just Brows-

ing and Information Gathering are equal in page view

duration.” Conﬁrming and refuting these hypotheses

in a ﬁeld study will draw a clearer picture of the task-

dependent user behavior and bring us closer to auto-

matic user task recognition.

To sum up, the ﬁndings indicate that the behav-

ioral attributes presented here can be useful for an

identiﬁcation of the user tasks, yet, there is still more

potential in the collected data for further analyses.

5 LIMITATIONS

The study was conducted with only 20 participants as

it was only meant to be exploratory. The output of this

study cannot be generally valid statements; it does,

however, reveal tendencies which will be translated

into hypotheses that allow a more exact investigation

on the relationship between user task and behavior.

The experiment was, moreover, conducted on one

Web site of an on-line newspaper. This was done

to reduce external inﬂuences on the behavior and to

guarantee that the test setting is as similar as possible

for each single participant. In a further study more

Web sites will have to be included as well as a wider

range in the age and the professional ﬁeld of the par-

ticipants.

Due to the age of the newspaper version a few

pages, mostly videos, were no longer available. In

this case, no page view was started as these situations

only lasted for a few seconds and the participants had

nothing to look at. Moreover, interactions with Flash

could not be captured, this involved, however, only

few multi-media pop-ups. Furthermore, changes of

a page’s content in the form of embedded JavaScript

or video were not recorded as these changes did not

really alter the page view itself.

Until now, security and privacy aspects have not

been considered as the data was collected locally. For

a remotely conducted study encrypted transfer of the

event logs has to be implemented. Moreover, a func-

tionality will be added to the event logging software

with which the users can control when they allow the

recording and on which Web sites. Concerning the fu-

ture application, the best protection could be guaran-

teed when the task derivation is realized on the local

computer and only the task name is sent to the Web

site where the personalization takes place.

6 SUMMARY

A study was presented which investigated the rela-

tionship between the task and the behavior of Internet

users. The study was exploratory as the main objec-

tive was to ﬁnd out which attributes of the behavior

are essential for a differentiation of user tasks. Mul-

tiple restrictions were set to guarantee that the partic-

ipants faced the same conditions. The investigation

focused on one version of an on-line newspaper to re-

duce the number of external inﬂuences in the form of

content and structure of the Web site. Three user tasks

were investigated, namely Fact Finding, Information

Gathering and Just Browsing, which were represented

as exercises the participants had to perform during

the experiment. By setting the tasks, the switching

between tasks and other distractions were prevented

and an unambiguous impression of the connection be-

tween behavior and task could be gained.

In this paper ﬁve behavioral attributes were intro-

USER BEHAVIOR UNDER THE MICROSCOPE - Can the Behavior Indicate a Web User’s Task?

221

duced, among them the average page view duration

and the number of news categories visited by the par-

ticipants. For each attribute at least two pairs of user

tasks showed a signiﬁcant difference. Using these at-

tributes, it is possible to differentiate between all three

tasks.

The next step will be to examine further behav-

ioral aspects, e.g. the scrolling. A question of par-

ticular importance will be whether the attributes nec-

essary to recognize a user task are general or can be

applied to only one kind of Web site. In the worst

case this would mean that a task recognition strategy

has to be developed for each type of Web site sepa-

rately with specialized attributes added to the general

attributes.

The ﬁndings of this study will be used for the

formulation of hypotheses describing the relationship

between user task and behavior. A further study in a

more natural surrounding with more participants and

thus more data will be conducted to prove these hy-

potheses. On the basis of the outcomes of this next

study, methods of automatic user task recognition can

be developed which will lead to a new quality of

user support. Knowing the user’s task means that the

user’s current needs are revealed. According to the

task, different services could be offered. Fact Find-

ers would certainly welcome a search functionality,

whereas forums concerning the topic which the users

are interested in can be offered when performing In-

formation Gathering. In the case of Just Browsing,

entertainment and distraction play an important role,

thus, these users might be interested in pictures and

videos. Moreover, the method with which link recom-

mendations are found could be altered; in the case of

Fact Finding, text mining is applicable whereas meth-

ods like the association rule mining can be used for

the other two tasks. However, Information Gathering

requires that the recommendations refer to the topic

which is currently of interest. These are only a few

of the manifold applications of user task recognition

which makes clear how important further investiga-

tions on this topic are.

ACKNOWLEDGEMENTS

We would like to thank Friedemann W. Nerdinger and

Stefan Melchior from the chair of Economic Psychol-

ogy at the University of Rostock for their support in

the study design. We also want to thank the peo-

ple who took part in the experiment. This work was

funded by the DFG (Graduate School 466).

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