Aspects of Context in Daily Search Activities
Survey about Nowadays Search Habits
Melyara Mezzi and Nadjia Benblidia
LRDSI Laboratory, Computer Science Department, Saad Dahlab University, Blida 1, Blida, Algeria
Keywords: Information Retrieval, Contextual Information Retrieval, Context-awareness, Search Trends.
Abstract: Over almost 70 years of perpetual improvement, Information Retrieval (IR), has had many approaches to
satisfy one’s information need. In this paper, we want to put forward and measure the importance of the
inclusion of a contextual dimension to enhance the relevance and effectiveness of a search task. Indeed, a
search task is no more concerned with a query and a set of documents only, but it is related to a wide range
of some extrinsic and intrinsic factors, so called “context”, which became a great challenge these last few
years. Besides the emergence and the significance of the use of context in IR, we conducted a survey with
434 internet users to understand their search trends and habits. The results and discussion may contain
valuable information for future researchers.
1 INTRODUCTION
Information seeking behaviour is rooted in a need to
find information (Han, Wang, M., and Wang, J.,
2010). According to Saracevic (2010), information
is anything that can change person’s knowledge.
Thus, the Information Retrieval (IR) process begins
with an anomalous state of knowledge (ASK). Then,
many changes in knowledge state are involved. In
short, IR is a purposeful process that alters the state
of knowledge reacting to an information need or
gap. A simple vision of an Information Retrieval
System (IRS) was believed to be as follows: (a) The
user expresses an Information need by formulating a
question (called query), (b) The IRS answers the
query and gives back results (texts, images,
videos,…etc), (c) The final phase is up to the user
who has to evaluate and reformulate her query if the
results do not satisfy her request. Today, this vision
became somehow obsolete, because the users, their
queries, and the desired information were believed
to be static. So, the relevance of a document was
computed statically between the query and the set of
documents ignoring the user, the device, the
environment, and the specificities around the search
activity which constitute the search context and are
as a matter of fact highly variable factors. Besides,
with the technology advances, information can
nowadays, be accessed everywhere and at anytime
which add to the variability and the uniqueness of
each search situation. And as no information is
context free, the inclusion of a contextual dimension
in the classic IR process became a real challenge.
To meet the expectations aforementioned, we
have conducted a short survey among 434 Internet
users in order to understand and analyze actual user
preferences and trends in the area of IR, especially
with the huge advances in search devices. We aim to
provide researchers with a good starting point in the
field of Contextual Information Retrieval (CIR).
The rest of the paper is structured as follows. In
section 2, we discuss the significance of context in
IR. After that, in section 3 we overview the most
important context’s components as an outcome to a
study performed on 16 works in the field of CIR.
Then, in the 4th section, we present our survey, its
analysis, and discussion. Finally, section 5 ends the
paper with a discussion and outlooks.
2 CONTEXT SIGNIFICANCE IN
INFORMATION RETIREVAL
According to Agbele et al (2012), context refers to
the circumstances in which an event (an IR
computing task in our case) takes place. In fact,
context is multi-layered; it extends beyond users or
systems. It is not self-revealing, nor it is self-evident,
but searchers do integrate context, which, they
understand intuitively, in IR theory and practice
627
Mezzi M. and Benblidia N..
Aspects of Context in Daily Search Activities - Survey about Nowadays Search Habits.
DOI: 10.5220/0005480706270634
In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 627-634
ISBN: 978-989-758-106-9
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
(Saracevic, 2010). In addition, IR task’s context is
any information whose change modifies the task’s
outcome (Agbele et al, 2012). Thus, an application is
believed to be “context-sensitive” or “context-
aware” if its structure and behaviour change
depending on the context so as to provide relevant
information and services for a given user. Research
activities on context-aware IR have increased
remarkably in recent years and many approaches
have been developed to automatically provide users
with information and services based on their current
situation (Mirceska, Trajkovic, and Ristevska,
2010). But unfortunately, they remain greatly
dependent on the field of application (smart-spaces,
weather-forecast, tour guides…). In fact, there are
no standards.
Context-aware computing was introduced for the
first time by Schilit, Adams, and Want (1994) who
state: “One challenge of mobile distributed
computing is to exploit the changing environment
with a new class of applications that are aware of
the context in which they are run”. After that, there
have been many definitions about the notion of
context in IR. One of the most approved definitions
is the one given by Dey (2001): “Context is any
information that can be used to characterize the
situation of an entity. An entity is a person, place or
object that is considered relevant to the interaction
between a user and an application, including the
user and application themselves. And by extension,
the environment, the application and the user are
embedded in”. In short, we can say that Context
includes all the intrinsic and extrinsic factors, which
are related to a given search task and whose the
direct or indirect inclusion in the IR process leads to
enhance, whether implicitly or explicitly its
effectiveness to convey the right information to the
searcher.
2.1 Features of the Information
Retrieval Task
As stated in (Saracevic, 2010), the information can
be: (a) objects in the world potentially conveying
information, (b) what is transferred from people or
objects to person’s cognitive systems, or (c)
components of internal knowledge in people’s mind.
Furthermore, according to Han, Wang, M., Wang, J.
(2010), the request for information can either be
external or self-initiated. In the same ground,
Saracevic (2010) talked about direct (end-user)
search and mediation search. Direct searchers are
people who seek information by and for themselves,
whereas in mediation search, there is an
intermediary who acts on the behalf of a person who
is actually seeking for information. The mediation
can either be informal when it comes to search
information for colleagues, family, and friends, or
formal when it comes to search for information as a
searcher or a teacher. Moreover, two kinds of search
are noticeable as reported by (Fujita and Oyama,
2011), (Daoud et al, 2009), and (Mirceska,
Trajkovic, and Ristevska, 2010): (a) Navigational
(evidential, or pull-based) search, and (b) Thematic
(informational, or push-based) search. Navigational
search deals with aware users having steady needs.
In this case, IR is explicit and the process consists of
comparisons with previous knowledge. Whereas, in
thematic search, the user inputs the query that
explains or describes information related to that the
user wishes to collect or research. Hence, IR is
implicit and the process consists of seeking for new
knowledge whether the needs are known, unknown
and poorly defined, or changing. Table 1 shows the
advantages and disadvantages of each kind.
Table 1: Navigational search VS thematic search.
Advantages Drawbacks
Navigational
search
Aware users
Clear needs
Overcommitted
users
Thematic
search
Smoother
experience
Fuzzy needs
To overcome the drawbacks of these search
methods, there is a need to contextualize the search
task.
3 CONTEXT’S COMPONENTS
Further to researches in the field of CIR, we can
observe that each search task is unique and comes
under a certain configuration of contextual factors.
However, some correlations can be found among a
set of search activities of the same user, between two
similar users, or between two disjoint users
performing a search task in a similar configuration
of contextual factors. According to Jilei (2010),
context fully describes the searcher, her device, and
her surroundings using a wide range of sensed and
historic information which forms the backbone for a
completely new class of services. There is a real
need for categorizing context’s types or components
in order to spot the most useful ones according to a
given application. Effectively, nowadays, context is
more targeted than ever.
As Han, Wang, M., Wang, J. (2010), we agree
that task is the driving force that constitutes IR and
WEBIST2015-11thInternationalConferenceonWebInformationSystemsandTechnologies
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real information behaviour. In order to find if there
may be other contextual components, we choose
sixteen valuable works that made use of context for
different purposes. Our goal was to deepen our
comprehension of the notion of context according to
different use cases and to come out with a
categorization of the context factors. What can be
noticed is that the use of contextual factors differs
from one application to another. Thus, the related
works are given just as valuable resources to enrich
future researchers with leading theories, models, and
results in the area of Contextual IR (CIR).
We find that the IR task is usually interlaced with
seven contextual components (table 2), namely:
user, queries, device, time, location, environment,
and documents. We restricted our focus to those
seven contextual factors and to test their coverage,
we conducted a short survey about search habits.
4 SURVEY ABOUT NOWADAYS
SEARCH HABITS
In order to understand the trends and users’ intents
Table 2: Important contextual factors in an Information Retrieval task.
Components Example Sources Related works
Search task
Personal calendars
can be used to
discover user’s
current task
Forms, events in the
calendars, query logs,
feedback
(Abowd et al, 1999), (Dey, 2001), (Belkin et al,
1999), (Bertrand, Egyed-Zsigmond, Calabretto, 2012),
(Ingwersen and Järvelin, 2005), (Kamvar and Baluja,
2006), (Poveda et al, 2010), (Saracevic, 2010).
User
Sana usually
browses
technology news
when waiting the
subway in working
days morning.
Profiling, user mining,
forms and feedbacks,
search logs, personal data
and content, contact list,
social network.
(Abowd et al, 1999), (Dey, 2001), (Belkin et al,
1999), (Bertrand, Egyed-Zsigmond, Calabretto, 2012),
(Bouidghaghen, 2009), (Daoud et al, 2009),
(Ingwersen and Järvelin, 2005), (Kessler, 2007),
(Kostadinov et al, 2004), (Poveda et al, 2010), (Ryan,
Pascoe, and Morse, 1999), (Ryu et al, 2010),
(Saracevic, 2010), (Tamine and Bahsoun, 2006).
Queries - -
(Belkin et al, 1999), (Bouidghaghen, 2009), (Daoud et
al, 2009), (Ryu et al, 2010).
Device
The doctor uses
her tablet in a
hospital to search
about the suitable
diagnosis.
Composite Capabilities/
Preference Profile
(CC/PP) proposes an
infrastructure to describe
device capabilities and
user preferences. Used for
content presentation
(Poveda et al, 2010).
(Bertrand, Egyed-Zsigmond, Calabretto, 2012),
(Ingwersen and Järvelin, 2005), (Kessler, 2007),
(Poveda et al, 2010), (Ryan, Pascoe, and Morse,
1999), (Ryu et al, 2010).
Time
According to a
time where a user
search for a
restaurant we can
deduce the type of
food he’s
searching for
“break-fast”,
“lunch”, …etc
System clock, calendars,
(Abowd et al, 1999), (Bertrand, Egyed-Zsigmond,
Calabretto, 2012), (Bouidghaghen, 2009), (Brown
and Jones, 2001), (Kessler, 2007), (Poveda et al,
2010), (Ryan, Pascoe, and Morse, 1999), (Ryu et al,
2010).
Location
City guides,
weather
forecasting,
products and
services marketing
We can use infrared,
Bluetooth and WIFI signal
strength to determine
indoor locations and GPS
for outdoor locations.
(Abowd et al, 1999), (Bertrand, Egyed-Zsigmond,
Calabretto, 2012), (Bouidghaghen, 2009), (Brown and
Jones, 2001), (Kessler, 2007), (Poveda et al, 2010),
(Ryan, Pascoe, and Morse, 1999), (Ryu et al, 2010).
Environment
Find all the
participants for a
meeting saved as
an event in the
calendar.
Environment sensors,
device pervasiveness
(Bluetooth,
accelerometers…)
(Abowd et al, 1999), (Bertrand, Egyed-Zsigmond,
Calabretto, 2012), (Bouidghaghen, 2009), (Brown and
Jones, 2001), (Kessler, 2007), (Poveda et al, 2010),
(Ryan, Pascoe, and Morse, 1999), (Ryu et al, 2010),
(Saracevic, 2010), (Tamine and Bahsoun, 2006).
Documents -
Web, intranet, or personal
texts, images, videos…etc
(Belkin et al, 1999), (Ingwersen and Järvelin, 2005),
(Saracevic, 2010).
AspectsofContextinDailySearchActivities-SurveyaboutNowadaysSearchHabits
629
in IR and come out with significant patterns for our
upcoming research in CIR, we conducted a short
survey among 434 anonymous online users (mostly
Facebook and Linkedin users).
4.1 Sample Data
Based on the afore-mentioned influential context
factors that can be found in the literature, ten leading
questions have been formulated and formatted.
Then, we broadcasted the Google Form link through
some social network groups and also provided a
printed version to students (about 12% of the
participants). The participants were from 27
nationalities which contributed to enrich the study,
but unfortunately, since the study was carried out
online many socio-demographic categories have
been excluded. Furthermore, despite the fact that
many similar surveys have been conducted already,
our main focus was to understand the habits and
preferences behind actual daily search tasks
knowing that several technological advances
occurred this last decade. We took special care to
formulate the study in the simplest possible form in
order to provide researchers in the field of CIR with
a clear view about contemporary search
preoccupations.
The survey motivated users for details
surrounding their daily search activities, presented as
yes/ no, single response, and multiple choices
questions including sections for suggestions. First,
users were invited to provide background
information about their gender, age, activity, and
whether they own a Smartphone or not. Table 3
summarizes the participant types.
More precisely, the study concerned six leading
issues: (a) favourite source of information, (b)
favourite devices used for search activities, (c)
favourite search categories, (d) number of keywords
usually used, (e) most important contextual factors,
and (f) collaboration in the search activity.
4.2 Results and Discussion
In the following, we will try to analyze the results.
But before, it is important to mention that for a deep
understanding of them, we performed a cross
tabulation analysis, which shows -mostly- very
harmonious results regardless to the different types
of demographic categories.
4.2.1 Favourite Source of Information
About 62.2% of the searchers concede preferring
‘Famous search engines’ to perform their search
Table 3: Socio demographic categories of the respondents’
sample.
Gender Education 19,6%
Female 52,8% Research 30,4%
Male 47,2% Industry 11,1%
Age Commerce 3,2%
Under 18 0,3% Unemployed 3,9%
18 – 29 44,2% Retired 0,9%
30 – 49 32,7% Other 8,1%
50+ 22,8% Smartphone owner
Activity Yes 71,9%
Student 22,8% No 28,1%
activities whereas, 19.7% choose ‘Social networks’
and 10.9% ‘Forums’. Besides, a minority of
searchers 5.1% and 2.1% admit using, respectively,
‘Mobile applications’ and ‘other sources of
information’ like dedicated web portals, less known
and more targeted search engines, digital libraries,
internal society or university databases, library
catalogues, faceted search engines, personal content
(documents, emails, and bookmarked web pages),
and finally, computational knowledge engines such
as wolfram. Unfortunately, these results (Figure 1)
gave rise to our apprehension about the preference
of researchers towards famous search engines,
which, are agreed to provide powerful search results
for trivial queries. But, they lose out personalization
and customization of the results according to internet
surfers’ needs and purpose. And consequently, they
miss effectiveness if the needs are unknown, dealing
with thematic search activities for example.
Figure 1: Search methods statistics.
4.2.2 Favourite Devices Used for Search
Activities
Results show (Figure 2) that 39% of searchers use
mostly ‘Laptops’ while searching, whereas 23.1%
still prefer ‘Desktops’, 21% ‘Smartphone’, 15%
‘Tablets’, and only 1.9% of searchers use their
‘Mobile phones’ in daily search tasks. This means
that despite the spread of mobile technologies,
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people still make use of desktops when it comes to
perform their daily search activities. Moreover, we
can notice that among all the mobile devices, laptops
are the favourite, and it is quite understandable
because of their ease of use in terms of interaction
fluency, query typing, and clarity of results
presentation.
Figure 2: Search devices statistics.
4.2.3 Favourite Search Categories
Concerning favourite search categories, unlike the
study of Kamvar and Baluja (2006), we found that
‘Technology’ outclasses the other categories with
19.69%, nearly followed by ‘News and events’ with
16.41%, and ‘Science’ 14.86%. The remaining
proposed categories obtained the scores showed in
table 4, beginning with the highest.
Table 4: Results of search categories.
Categories Responses Categories Responses
Technology 19.69%
Society &
communi-cation
6.11%
News & events 16,41% Local services 5.56%
Science 14,86% Sport 4.83%
Entertainment 8.75% Games & hobbies 4.10%
Health & foo
d
8.57% Industry 3.01%
Travels 6.65% Others 1.46%
Despite the differences between the mentioned
search categories, we wanted to find some patterns
concerning the types of needs behind the queries.
The survey results show, analogically to the study of
Broder (2002), that the respondents were most
willing to perform informational (thematic) search
than navigational one.
4.2.4 Number of Keywords per Query
The 44.7% of respondents admitted using from one
to three keywords, 44.5% from four to six, and
10.8% more than six keywords. Undeniably, the less
keyword, the searcher uses, the harder it is for the
IRS, to please their need of information. For
instance, we have noticed that 45.51% of
Smartphone users utilize from one to three
keywords, whereas 43.26% use from four to six.
Contrariwise, this trend was reversed for
respondents without Smartphones with, respectively,
42.62% and 47.54% as shown in figure 3.
Figure 3: Keywords statistics for Smartphone and non-
Smartphone users.
These results resemble barely to the study
conducted by Kamvar and Baluja (2006), who
reported that mobile users’ queries are shorter and
therefore more ambiguous. Indeed, we remark that
the two sample results (i.e. smartphone and non-
smartphone users) are nearly similar and this is due
to the technological advance concerning
smartphones that are nowadays as powerful as some
laptops. Nevertheless, the results obtained in this
section about keywords, indicates the need to rely on
the context factors surrounding the search activity.
4.2.5 Most Important Contextual Factors
We noticed that the most important contextual
factors (Figure 4) are: ‘Accuracy’ with 38.29%, then
‘Time’ (freshness of the information) with 23.9%,
followed by ‘Results and content personalization’
with 12.13%, ‘Personal preferences’ with 11.3%,
‘Location’ with 11.18%, and ‘Social network
preferences’ with 2.85%. Finally, 0.36% of
respondents chose the option ‘Other’, and gave some
suggestions. We retain trustworthiness and
genuineness of the information sources, the results’
ranking and referencing, and website speed. This
question was somehow the core of our study, since
our main focus was about the importance of
extrinsic and intrinsic contextual factors in any
search activity.
The two most interesting outcomes are:
The accuracy and freshness of the
information are more important than their
relation to the notion of location. This differs
from the perspective of Ryu et al (2010), who
AspectsofContextinDailySearchActivities-SurveyaboutNowadaysSearchHabits
631
classified contextual factors that prompt
information needs as follows-beginning with the
most influent: location, time, conversation, and
activity, and also Kamvar and Baluja (2006) who
classified them as follow: activity, location, time,
and conversation. Instead, this confirms the trend
concerning the interaction at a very large scale
(allowed by social networks mostly), where,
everyone is a world citizen without known
boundaries, nor territorial limitations of
knowledge.
The limit between ‘Personal preferences’ and
‘Social network preferences’ is small. That is to
say people do take into account the view of their
(physical and virtual) social network
proportionally to their own ‘Personal
preferences’. According to Evans and Chi
(2008), external environment (i.e. people) may
be valuable information resources for one’s
information search process. In their paper, Evans
and Chi (2008) state that recently, searchers have
observed direct user cooperation during web-
based information seeking. Active collaboration
may occur under some circumstances, where
users interact together remotely, asynchronously,
and even involuntarily and implicitly. They are
indeed, influenced by their friends, collaborators,
as well as by their social network. This is why
the opinion of this latter is as important as their
own yet most people do prefer performing their
research alone as found in the question
concerning the collaboration in research.
Figure 4: Statistics about the most important contextual
factors.
4.2.6 Collaboration in Search Activity
84.33% of respondents concede that they rather
perform a search activity alone. Whereas, 11.06%
prefer being surrounded by real (physical) friends,
and 4.15% choose to rely on their social network
circles. Moreover, 0.46% of respondents gave
suggestions that support overall that most searches
are performed independently, but at times can be
conducted collaboratively. This does depend on the
need. These results (Figure 5) support that
effectively, the IR task can either be external or self-
initiated.
Figure 5: Search activity statistics.
5 CONCLUSIONS
Throughout years and with the advance of
technology, search task became more flexible,
allowing a wider range of choices between different
sources of information, devices, and search
categories. Moreover, the perspective of an eventual
collaboration became possible, regardless of the
location of the different searchers. In this paper, the
significance of the inclusion of a contextual
dimension was discussed. Moreover, we tried to
inquire about actual search trends taking into
account the technological advances. Thus, we
introduced our short survey with its detailed results
and analysis, which we expect will provide future
researchers with valuable information. We retain the
inclination of users towards: (a) social network
preferences proportionally to their own personal
preferences, also (b) users concern about accuracy
and time, and finally (c) shorter and thus more
ambiguous queries. Consequently, our upcoming
work will consist on the formalization and testing of
a CIR model centred on the IR task.
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