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Understanding User Goals in Web Search


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Previous work on understanding user web search behavior has focused on how people search and what they are searching for, but not why they are searching. In this paper, we describe a framework for understanding the underlying goals of user searches, and our experience in using the framework to manually classify queries from a web search engine. Our analysis suggests that so-called "navigational" searches are less prevalent than generally believed, while a previously unexplored "resourceseeking " goal may account for a large fraction of web searches. We also illustrate how this knowledge of user search goals might be used to improve future web search engines.
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Understanding User Goals in Web Search
Daniel E. Rose
Yahoo! Inc.
701 First Avenue, MS B201
Sunnyvale, CA 94089 USA
+1 408 349 7992
Danny Levinson
Yahoo! Inc.
144 Fourth Avenue SW, Suite 2600
Calgary AB T2P 3N4 Canada
+1 403 303 4590
Previous work on understanding user web search behavior has
focused on how people search and what they are searching for,
but not why they are searching. In this paper, we describe a
framework for understanding the underlying goals of user
searches, and our experience in using the framework to manually
classify queries from a web search engine. Our analysis suggests
that so-called “navigational” searches are less prevalent than
generally believed, while a previously unexplored “resource-
seeking” goal may account for a large fraction of web searches.
We also illustrate how this knowledge of user search goals might
be used to improve future web search engines.
Categories and Subject Descriptors
H.3.3 [Information Storage and Retrieval]: Information Search
and Retrieval – search process; H.4.m [Information Systems
Applications]: Miscellaneous.
General Terms
Measurement, Experimentation, Human Factors
Web search, information retrieval, user behavior, user goals,
query classification.
If we imagine seeing the world from the perspective of a search
engine, our only view of user behavior would be the stream of
queries users produce. Search engine designers often adopt this
perspective, studying these query streams and trying to optimize
the engines based on such factors as the length of a typical query.
Yet this same perspective has prevented us from looking beyond
the query, at why the users are performing their searches in the
first place.
The “why” of user search behavior is actually essential to
satisfying the user’s information need. After all, users don’t sit
down at their computer and say to themselves, “I think I’ll do
some searches.” Searching is merely a means to an end – a way to
satisfy an underlying goal that the user is trying to achieve. (By
“underlying goal,” we mean how the user might answer the
question “why are you performing that search?”) That goal may
be choosing a suitable wedding present for a friend, learning
which local colleges offer adult education courses in pottery,
seeing if a favorite author’s new book has been released, or any
number of other possibilities. In fact, in some cases the same
query might be used to convey different goals – for example, the
query “ceramics” might have been used in any of the three
situations above (assuming it is also the title of the book in
What difference would it make if the search engine knew the
user’s goal? At the very least, the engine might provide a user
experience tailored toward that goal. For example, the display of
relevant advertising might be welcome in a shopping context, but
unwelcome in a research context. In fact, we have argued
elsewhere [10] that goal-sensitivity will be one of the crucial
factors in future search user interfaces. But the potential to
capitalize on this goal sensitivity goes beyond the user interface.
The underlying relevance-ranking algorithms that determine
which results are presented to users might differ depending on the
search goal. For example, queries that express a need for advice
might rely more on usage- or connectivity-based relevance
factors, while those involving open-ended research might weight
traditional information retrieval measures (such as term
frequency) more highly.
Our premise is that web searches reflect a diverse set of
underlying user goals, and that knowledge of those goals offers
the prospect of future improvements to web search engines.
Achieving these improvements is an ambitious project involving
three primary tasks. First, we need to create a conceptual
framework for user goals. Second, we need a way for search
engines to associate user goals with queries. Third, we need to
modify the engines in order to exploit the goal information.
In this paper we focus on the first task, and the initial parts of the
second: characterizing user search goals and examining the
problem of inferring goals from query behavior. We begin in
section 2 by looking at previous work on understanding
information-seeking behavior. Next, in section 3, we describe our
model of search goals. In section 4, we review the methodology
used to classify queries using our model, and we provide some
results from this analysis. We conclude with some final thoughts
about the applicability of this work.
Studies of user search behavior have a long history in Information
and Library Science. These include studies of the reference
interview process, long before most users had access to computer-
assisted search tools. When search engines first became available
for use by researchers, many studies were conducted that
attempted to understand user search behavior in an online context.
For example, Bates [4] looked at the different ways in which
people performed searches, and later proposed ways to
characterize the overall search process [5]. Belkin’s Anomalous
States of Knowledge (ASK) framework was an early attempt to
Copyright is held by the author/owner(s).
WWW 2004, May 17–22, 2004, New York, New York, USA.
ACM 1-58113-844-X/04/0005.
model the cognitive state of the user and then translate this
understanding into a practical design for an information retrieval
system [6]. Included in the ASK study was an analysis of some of
the different types of information needs of different users. For
example, one type of ASK was summarized as “Well-defined
topic and problem,” while another was “Information needed to
produce directions for research.”
Once web search engines became available and popular, studies of
web search behavior followed quickly. For example, Silverstein
et al. conducted an analysis of query logs from the AltaVista
search engine, confirming some of the original findings of web
search use, such as the predominance of very short queries [11].
A summary of many of the early studies may be found in Jansen
and Pooch’s 2000 review [9].
One of the most comprehensive attempts to understand web
search behavior has been the ongoing research of Spink and her
colleagues, who analyzed query logs of the Excite search engine
from 1997, 1999, and 2001 [13]. Although there have been some
changes in user behavior during this period (such as a decrease in
willingness to look at more than one page of search results), Spink
et al. found that general search strategies have remained fairly
Prior to the advent of the worldwide web, search engine designers
could safely assume that users had an “informational” goal in
mind. That is, users’ reason for searching was generally to “find
out about” their search topic. This was due both to the nature of
the population with access to full-text search engines (students,
researchers, lawyers, intelligence analysts, etc.) and to the nature
of the databases that could be searched (with services such as
Westlaw, Dialog, Medline, Lexis/Nexis, etc.)
But in the web era, search engines are used for more than just
research. Even the most cursory look at the query logs of any
major search engine makes it clear that the goals underlying web
searches are many and varied. And while the vast body of work
described above has helped us to understand what users are
searching for and how their information-seeking process works,
there have been few attempts to look at why users are searching.
One of the few exceptions is Broder’s “Taxonomy of Web
Search” [7]. Motivated by the idea that the traditional notion of
an “information need” might not adequately describe web
searching, Broder came up with a trichotomy of web search
“types”: navigational, informational, and transactional.
Navigational searches are those which are intended to find a
specific web site that the user has in mind; informational searches
are intended to find information about a topic; transactional
searches are intended to “perform some web-mediated activity.”
Our first task was to understand the space of user goals. In
particular, we needed to come up with a framework that could
identify and organize a manageable set of canonical goal
categories. These goal categories, in turn, must encompass the
majority of actual goals users have in mind when searching.
To develop the goal framework, we looked at a sample of queries
from the AltaVista search engine [1]. We brainstormed a variety
of goal possibilities, based on our own experiences, some
previous internal query analysis at AltaVista, and a preliminary
examination of the query set. This resulted in a flat list of goals.
This list served as a basis for an initial goal classification
framework, which we then used to categorize a sample of 100-200
queries. Next, we revised the framework to accommodate the
results of the classification test. Categories were modified, or new
categories added, when queries did not fit the existing framework.
Some goal categories proposed early on, such as “finding a place
in the world” (e.g. a map request), were dropped as
unrepresentative. Some categories were merged, some were split
more finely, and some entirely new ones arose. This propose-
classify-refine cycle was repeated three times, each with a new set
of queries.
One of our early findings was that there were many cases where
the goal of the search was neither to find a web site nor to get
information, but simply to get access to an online resource. For
example, a query such as beatles lyrics suggests not a
desire to learn about lyrics to Beatles songs, but simply a desire to
view the lyrics themselves. This led to the creation of a broad
new goal category that we call resource searches. We believe
these resource searches are a relatively neglected category in the
search engine world.
As we repeatedly revised the set of goal categories, we gradually
reached the conclusion that the goals naturally fell into a
hierarchical structure. In fact, the top level of the hierarchy
resembles Broder’s trichotomy, but our more general “resource”
category replaces his notion of “transactional” queries. Our
resulting goal framework is shown in Table 1.
We define the navigational goal as demonstrating a desire by the
user to be taken to the home page of the institution or
organization in question. To be considered navigational, the
query must have a single authoritative web site that the user
already has in mind. For this reason, most queries consisting of
names of companies, universities, or well-known organizations
are considered navigational. Also for this reason, most queries for
people – including celebrities – are not. A search for celebrities
such as Cameron Diaz or Ben Affleck typically results in a variety
of fan sites, media sites, and so on; it’s unlikely that a user
entering the celebrity name as a query had the goal of visiting a
specific site.
Informational queries are all focused on the user goal of
obtaining information about the query topic. This category
includes goals for answering questions (both open- and closed-
ended) that the user has in mind, requests for advice, and
undirected” requests to simply learn more about a topic.
Undirected queries may be viewed as requests to “find out about”
or “tell me about” a topic; most queries consisting of topics in
science, medicine, history, or news qualify as undirected, as do
the celebrity queries mentioned above. Note that the two question-
goal categories do not require that the user explicitly express the
query in the form of a question; the query “last czar of russia” is
reasonably interpreted as a closed-class question “who was the
last czar of Russia?” Similarly, queries in the “advice” category
may take many forms.
The informational goal class also includes the desire to locate
something in the real world, or simply get a list of suggestions for
further research. Most product or shopping queries have the
“locate” goal – I’m searching the web for X because I want to
know where I can buy X. Plural query terms are a highly reliable
indicator of the list goal.
Resource queries all represent a goal of obtaining something
(other than information). If the resource is something I plan to
use in the offline world, such as song lyrics, recipes, sewing
patterns, etc., we call it an “obtain” goal. If the resource is
something that needs to be installed on my computer or other
electronic device to be useful, the goal is “download.” If my goal
is simply to experience (typically view or read) the resource for
my enjoyment, the goal is “entertain.” The most common
example of queries with an entertainment goal were those that
dealt with pornography. Finally, the “interact” goal occurs when
the intended result of the search is a dynamic web service (such as
a stock quote server or a map service) that requires further
interaction to achieve the user’s task.
The search goal framework described above proved to be both
stable (requiring no major revisions as new queries were
encountered) and comprehensive (encompassing the goals of all
the queries we had seen). We were therefore able to move on to
the second major task, associating goals with queries.
There are two ways a search engine might associate goals with
queries at runtime: either the user can identify the goal explicitly
through the user interface, or the system can attempt to infer the
goal automatically. Google’s “I’m feeling lucky” feature [8], in
which users implicitly identify their goal as “navigate to a specific
web site,” may be thought of as an early example of the first
Table 1: The Search Goal Hierarchy. Queries are only assigned to leaf nodes.
All examples are taken from actual AltaVista queries.
1. Navigational
My goal is to go to specific known website that I already
have in mind. The only reason I'm searching is that it's
more convenient than typing the URL, or perhaps I don't
know the URL.
aloha airlines
duke university hospital
kelly blue book
2. Informational My goal is to learn something by reading or viewing web
2.1 Directed I want to learn something in particular about my topic
2.1.1 Closed
I want to get an answer to a question that has a single,
unambiguous answer.
what is a supercharger
2004 election dates
2.1.2 Open
I want to get an answer to an open-ended question, or one
with unconstrained depth.
baseball death and injury
why are metals shiny
2.2 Undirected
I want to learn anything/everything about my topic. A query
for topic X might be interpreted as "tell me about X."
color blindness
jfk jr
2.3 Advice I want to get advice, ideas, suggestions, or instructions. help quitting smoking
walking with weights
2.4 Locate My goal is to find out whether/where some real world
service or product can be obtained
pella windows
phone card
2.5 List
My goal is to get a list of plausible suggested web sites (I.e.
the search result list itself), each of which might be
candidates for helping me achieve some underlying,
unspecified goal
amsterdam universities
florida newspapers
3. Resource My goal is to obtain a resource (not information) available
on web pages
3.1 Download
My goal is to download a resource that must be on my
computer or other device to be useful
kazaa lite
mame roms
3.2 Entertainment
My goal is to be entertained simply by viewing items
available on the result page
xxx porno movie free
live camera in l.a.
3.3 Interact
My goal is to interact with a resource using another
program/service available on the web site I find
measure converter
3.4 Obtain
My goal is to obtain a resource that does not require a
computer to use. I may print it out, but I can also just look
at it on the screen. I'm not obtaining it to learn some
information, but because I want to use the resource itself.
free jack o lantern patterns
ellis island lesson plans
house document no. 587
approach. The second approach would involve automatic
classification using statistical or machine learning methods; these
methods in turn will require hundreds or thousands of examples of
classified queries (and their associated features) as training
In either case, we need to know the relative prevalence of various
goals. And if we hope to infer goals automatically in the future,
we need to know that it is possible to do so manually. This section
describes our work on these initial aspects of the problem; the
remaining parts of the task will be the focus of future work.
4.1 Manual Query Classification
In order to definitively know the underlying goal of every user
query, we would need to be able to ask the user about his or her
intentions. Clearly, this is not feasible in most cases. But can the
goal be determined simply by looking at the query itself, or is
more information required?
We believe that in many cases, user goals can be deduced from
looking at user behavior available to the search engine. Included
in this behavior are the following:
the query itself
the results returned by the search engine
results clicked on by the user
further searches or other actions by the user.
We wanted to determine whether this was sufficient information
for a human to consistently classify queries according to our goal
framework. Once we could successfully classify queries
manually, we would be able to provide training data for a future
automatic classification system.
To facilitate the task of manual classification, we created a
research tool that provided these four types of information for sets
of queries taken from the AltaVista query logs. A screen shot of
the classification tool interface is shown in Figure 1.
Figure 1: A screenshot of the tool used to assist manual query classification.
The query (kelly blue book in this example) appears in the
gray-highlighted box at upper left. To the right of the query are
links which lead to the search results that appear when the query
is executed on two major search engines. Beneath this is a table
of search engine events (clicks and queries) that this user
performed following the initial query. In this case, we see that six
seconds after issuing the query, the user entered a new, more
specific query on the same topic. (The syntax suggests that this
query resulted from the user clicking on a suggested query
refinement term using AltaVista’s Prisma [2, 3] assisted search
tool.) Eight seconds later, he or she clicked on the first result,, which is the home page of the Kelly Blue Book
(a publication that gives guidelines for new and used car prices).
Thus a human classifier using the tool (namely, one of the
authors) concluded that the underlying user goal for this query
was “navigational,” and selected the corresponding radio button.
When the “Submit classification” button is pressed, a new query
is displayed, together with its corresponding information. In the
example shown, a human classifier could probably have guessed
the goal simply by viewing the initial query. Yet there are cases
where each of the sources of information played a role in
assessing the user’s goal.
Consider the query final fantasy. This is the name of a
series of popular computer games. Did the user want to find a
place to buy one of the games (a “locate” goal)? Did he or she
intend to go to some official Final Fantasy web site (a
“navigational” goal)? A look at the search results on AltaVista
and Google shows that there isn’t an authoritative web site for the
game. The game’s manufacturer has a web site, but it covers
many games, has no specific page for the entire Final Fantasy
series, and is ranked #3 on both AltaVista and Google. This casts
some doubt on likelihood of a navigational goal. The result list
contains many sites with information about the games, and many
sites where one can buy the games. The user’s event history,
shown in Table 2, provides further information.
The user examined the result list for 36 seconds, then visited the
web site, described as “an unofficial
guide to Final Fantasy.” About a minute later, s/he returned to
the original query, and then chose a different web site, “Eyes on
Final Fantasy,” (, a site containing news
and information about the games. This pattern indicates that the
user was not interested in buying the game, but simply wanted
some sort of information about it – perhaps the latest news about
future releases. In this case, we’d conclude that the underlying
goal was “undirected” information.
4.2 Results
Three sets of approximately 500 U.S. English queries1 each were
randomly selected from the AltaVista query logs on different days
and at different times of the day. These were manually classified,
one set using the classification tool as described above, and two
sets using an earlier version that did not contain the user’s event
history. Results are shown in Table 3. (Note that the “open” and
“closed” categories have been collapsed into a single “directed”
category, due to the low numbers of results.)
It is interesting to note that nearly 40% of queries were non-
informational in every case, and a large fraction of the
informational queries appeared to be attempts to locate a product
or service rather than to learn about it. In fact, just over 35% of
all queries appeared to have the kind of general research goal
(questions, undirected requests for information, and advice-
seeking) for which traditional information retrieval systems were
It is also interesting that the relative distributions of goal
categories are quite similar across the different query sets, despite
the fact that they represented different dates during the year and
different times of day. Perhaps more importantly, the additional
information about user click behavior used in the Set 3 results did
not result in a substantially different mix of goals. Although this
requires further study, it suggests the surprising result that goals
can be inferred with almost no information about the user’s
Because the top level of our goal classification framework is
similar to Broder’s web search taxonomy [7], we also examined
how the distribution of our queries into the top-level goal
categories compared with his. Broder used two methods to
classify queries, a user survey and manual classification of log
entries. The survey had one question intended to identify
navigational queries, and one that allowed users to choose any of
several tasks (shopping, downloading, etc.) that he considered
“transactional.” If none of these tasks was chosen, the query was
assumed to be informational. The log analysis followed a similar
decision procedure. Broder also eliminated sexually oriented
queries, which accounted for about 10% of the data.
Figure 2 compares our top-level goal classification with results
reported by Broder. (We are simplifying somewhat by equating
Broder’s “transactional” category with our more general
“resource” goal.) We consistently found a greater proportion of
informational queries, and a smaller proportion of navigational
1 The number was not exact because we started with a larger set
and then discarded those that were either not English or used
non-standard search operators such as “link:”.
Table 2: Events following the query final fantasy.
Time Delta t Event Details
36 36 result click pg 1, pos 1
113 77 query pg 1 final fantasy
118 5 result click pg 1, pos 8
147 29 result click pg 1, pos 8
36 30 24.3 27 25
39 48 60.9 61.3 61.5
24.5 20 14.7 11.7 13.5
Broder user
Broder log
Current study,
set 1
Current study,
set 2
Current study,
set 3
Resource / Transactional Informational Navigational
Figure 2: Comparison of Broder’s search taxonomy to our top-level goals. Resource and informational
results in the first column are Broder’s estimates. Results do not total 100% due to rounding error.
and resource/transaction queries than the earlier study. While the
differences in informational and resource/transactional queries
may be accounted for by our different definitions of those
categories, this does not account for the large difference in the
fraction of navigational queries.
In fact, since Broder sampled all queries, while we sampled only
session-initial queries, the actual difference in navigational query
rates may be even higher. This is because navigational query
sessions are likely to be shorter and thus overrepresented in our
session-initial measure. However, it is not clear that this had any
more impact than the other methodological differences used to
obtain our respective data sets.
If our findings about the relatively small number of navigational
queries are accurate, they suggest that much of the attention in the
commercial search engine world may be misdirected. Tests such
as the “Perfect Page Test” organized by one search engine
newsletter [12] encourage search engine providers to focus on
performance on navigational queries, even though this does not
appear to reflect the majority of user needs.
Table 3: Results of Classifying Queries by Search Goals
directed 2.70% 3.30% 7.30%
undirected 31.30% 26.50% 22.70%
advice 2.00% 2.70% 5.00%
locate 24.30% 25.90% 24.40%
list 2.70% 2.90% 2.10%
informational total 63.00% 61.30% 61.50%
download 4.30% 4.30% 5.60%
entertain 4.00% 8.20% 5.80%
interact 5.70% 4.30% 6.00%
obtain 7.70% 10.30% 7.70%
resource total 21.70% 27.00% 25.00%
navigational 15.30% 11.70% 13.50%
In analyzing our results, we are aware of certain limitations that
may restrict the generalizability of our conclusions. One issue is
that we have no way of knowing conclusively whether the goal we
inferred for a query is in fact the user’s actual goal. In the future,
we would like to combine our work with user studies, including
qualitative data such as diary reports of user goals. In order to do
this, we first need to make sure that our goal framework and
classification methodology can be used by judges other than the
A second issue is that the AltaVista user population may not be
representative of search engine users in general. In particular,
AltaVista’s reputation for providing more powerful query tools,
combined with its relatively small market share, may make it the
engine of choice for users with difficult informational queries, but
not a first choice for typical users issuing common queries. It is
possible that this may account for some of the user behavior we
saw, despite the fact that we already excluded queries with
explicit Boolean syntax or other advanced search operators. In
order to investigate this issue, we hope to extend our research to
Yahoo! search users.
If web search engines are to continue to improve in the future,
they will need to take into account more knowledge of user
behavior – not just how people search, but why. We have created
a framework for understanding the underlying goals of search, and
have demonstrated that the framework can be used to associate
goals with queries given limited information.
This analysis of user goals has already yielded two unexpected
patterns in web search. First, so-called “navigational” queries
appear to be much less prevalent than generally believed. Second,
many queries appear to be motivated by a previously unexplored
goal involving the need to obtain online and offline resources.
More importantly, an understanding of search goals provides a
foundation for tackling the larger problems of conveying user
goals to a search engine (either explicitly or by inference), and
modifying the engines’ algorithms and interfaces to exploit this
[1] AltaVista,
[2] AltaVista, description of Prisma query refinement tool,
[3] Anick, P. Using Terminological Feedback for Web Search
Refinement: A Log-Based Study. Proceedings of SIGIR
2003, 88-95.
[4] Bates, M.J. Information Search Tactics. Journal of the
American Society for Information Science, 30, July 1979,
[5] Bates, M.J. The Design of Browsing and Berrypicking
Techniques for the Online Search Interface. Online Review
13, October 1989, 407-424.
[6] Belkin, N.J., Oddy, R.N., and Brooks, H.M. ASK for
Information Retrieval: Part II. Results of a Design Study.
Journal of Documentation, 38(3), Sep. 1982, 145-164.
[7] Broder, A. A Taxonomy of Web Search. SIGIR Forum 36(2),
[8] Google, Description of “I’m Feeling Lucky” feature,
[9] Jansen, B.J. and Pooch, U. A Review of Web Searching
Studies and a Framework for Future Research. Journal of
the American Society of Information Science and
Technology, 52(3), 235-246, 2000.
[10] Rose, D.E. Reconciling Information-Seeking Behavior with
Search User Interfaces for the Web. Journal of the American
Society of Information Science and Technology, to appear.
[11] Silverstein, C., Henzinger, M., Marais, H., and Moricz, M.
Analysis of a Very Large Web Search Engine Query Log.
SIGIR Forum, 33(3), 1999. Originally published as DEC
Systems Research Center Technical Note, 1998.
[12] Sherman, C. and Sullivan, D. The Search Engine ‘Perfect
Page’ Test. Search Day 391 (Nov. 4, 2002),
[13] Spink, A., Jansen, B.J., Wolfram, D., and Saracevic, T. From
E-Sex to E-Commerce: Web Search Changes. IEEE
Computer, 35(3), 107-109, 2002.
... Jansen et al. [12] extended Broder's taxonomy by defining secondary and tertiary level intent classes for each of the three top level intents. Rose and Levinson [13] redefined Broder's taxonomy by introducing sub-levels and replacing the "transactional intent" with a "resource seeking intent." In this restructuring, at level 2, five sub-classes for the informational and four for the resource intent were formulated. ...
... Baez-Yates et al. [14] proposed a different taxonomy from the earlier research, and classified queries as informational, not informational and ambiguous. The intent taxonomies were used to annotate datasets extracted from publicly-released query logs, e.g., the TREC Web Corpus and WT10g collection ( test_collections/, accessed on 15 June 2022), AltaVista logs [13], DogPile [12], Lycos [15], MSN Search Query log (, ac- Table 2 highlights a balanced coverage of queries across all domains. ...
... Transactional queries consist of 13.82% of the dataset while navigational queries form 9.92%, respectively. These findings are in conformity with the log analyses of English queries reported in [5,12,13], where the informational queries were also in the majority followed by the transactional and navigational queries. Table 5 further presents the query length for each intent type as well as the maximum and minimum query lengths for each intent. ...
Full-text available
Detecting the communicative intent behind user queries is critically required by search engines to understand a user’s search goal and retrieve the desired results. Due to increased web searching in local languages, there is an emerging need to support the language understanding for languages other than English. This article presents a distinctive, capsule neural network architecture for intent detection from search queries in Urdu, a widely spoken South Asian language. The proposed two-tiered capsule network utilizes LSTM cells and an iterative routing mechanism between the capsules to effectively discriminate diversely expressed search intents. Since no Urdu queries dataset is available, a benchmark intent-annotated dataset of 11,751 queries was developed, incorporating 11 query domains and annotated with Broder’s intent taxonomy (i.e., navigational, transactional and informational intents). Through rigorous experimentation, the proposed model attained the state of the art accuracy of 91.12%, significantly improving upon several alternate classification techniques and strong baselines. An error analysis revealed systematic error patterns owing to a class imbalance and large lexical variability in Urdu web queries.
... En los últimos años, a raíz del éxito de los móviles, se ha incorporado una nueva intención de búsqueda recogida en la literatura bajo el nombre "visitar en persona", la cual está relacionada con las búsquedas que tienen como objetivo obtener información e indicaciones sobre cómo llegar a establecimientos o lugares de una determinada categoría cerca del usuario (Macià, 2019). El estudio de la intención de búsqueda de los usuarios, así como el análisis semántico de los términos utilizados en la ecuación de búsqueda han venido siendo estudiados en la última década en la literatura científica dentro del ámbito de la documentación, la informática y el marketing (Hulth, 2003;Rose y Levinson, 2004;Jansen et al., 2007;2008;Yin y Shah, 2010). ...
Los buscadores son el principal punto de acceso a los contenidos de los sitios web. El SEO es la práctica encaminada al aumento de la cantidad y calidad de tráfico hacia un sitio web a través de los resultados de búsqueda orgánicos procedentes de los buscadores. El trabajo SEO busca satisfacer ciertos factores de posicionamiento que tienen en cuenta los algoritmos de los buscadores en la ordenación de los resultados de búsqueda. En los últimos años hemos visto como estos algoritmos han ido virando hacia factores y señales orientados a priorizar aquellos resultados que mejor satisfacen la intención de búsqueda que se esconde tras la palabra clave utilizada, ofreciendo también la mejor experiencia de usuario posible en la página de destino. Tras un análisis bibliográfico de los factores relacionados con el análisis de la intención de búsqueda y los factores relacionados con la mejora de la experiencia de usuario desde un punto de vista SEO en el buscador de Google, se recogen un conjunto de acciones y estrategias que pueden implementarse con el objetivo de mejorar el posicionamiento de las páginas de un sitio web.
... Internet es un entorno único y el hábito del lector en la web es diferente a la lectura de la información escrita impresa (Cheng y Dunn, 2015). Según Rose y Levinson (2004), la percepción de un usuario sobre lo que es preciso, actual, importante o útil, no sólo está determinado por la información que está buscando, sino por la razón por la que la buscan. Este proceso de búsqueda conlleva, generalmente, que pocas veces miren más allá de los primeros enlaces web ofrecidos por defecto de Google y de otros motores de búsqueda (Irwin et al., 2021). ...
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La obra que se ha editado con el título “Innovación en escenarios educo-sociales”” coordinada por docentes de distintas universidades, recoge en sus siete capítulos una reflexión profunda de la innovación en contextos socioeducativos; a través de marcos teóricos, análisis de prácticas pedagógicas y experiencias innovadoras.
Cobbler’s children do not wear shoes. Software engineers build sophisticated software but we often cannot find the needed information and knowledge for ourselves. Issues are the amount of development information that can be captured, organizing that information to make them useable for other developers as well as human decision-making issues. Current architectural knowledge management systems cannot handle these issues properly. In this paper, we outline a research agenda for intelligent tools to support the knowledge management and decision making of architects. The research agenda consists of a vision and research challenges on the way to realize this vision. We call our vision on-demand architectural knowledge systems (ODAKS). Based on literature review, analysis, and synthesis of past research works, we derive our vision of ODAKS as decision-making companions to architects. ODAKS organize and provide relevant information and knowledge to the architect through an assistive conversation. ODAKS use probing to understand the architects’ goals and their questions, they suggest relevant knowledge and present reflective hints to mitigate human decision-making issues, such as cognitive bias, cognitive limitations, as well as design process aspects, such as problem-solution co-evolution and the balance between intuitive and rational decision-making. We present the main features of ODAKS, investigate current potential technologies for the implementation of ODAKS and discuss the main research challenges.
As an emerging audience engagement channel for news organizations, news chatbots can interact with and attract audiences in a conversational manner. The present study applies the comparative digital journalism frameworks and examines how society-level factors—such as media systems and information communication technology’s development—explain chatbot implementation on social media platforms. We surveyed 365 news organizations across 38 countries or regions and inspected their Facebook Messenger accounts with a mixed-methods approach. We found that less than half of the surveyed news organizations implemented Messenger, and only 67 Messengers were responsive—i.e. able to produce at least one response. We used the walkthrough method to interact with the Messengers with 22 pre-defined search queries on information seeking and navigation related to COVID-19. Then we used qualitative content analysis to examine the contents generated by the Messengers. Some Messengers are out of service or could only provide limited services (e.g. generating templated responses or closed-ended options). The Messengers in different news organizations demonstrated great variations in their capacity to understand the queries and interact with the audiences and reparative strategies to handle search failure. We proposed a three-category typology of news chatbots and offered practical and constructive suggestions for news organizations.
Domain specialists such as council members may benefit from specialised search functionality, but it is unclear how to formalise the search requirements when developing a search system. We adapt a faceted task model for the purpose of characterising the tasks of a target user group. We first identify which task facets council members use to describe their tasks, then characterise council member tasks based on those facets. Finally, we discuss the design implications of these tasks for the development of a search engine. Based on two studies at the same municipality we identified a set of task facets and used these to characterise the tasks of council members. By coding how council members describe their tasks we identified five task facets: the task objective, topic aspect, information source, retrieval unit, and task specificity. We then performed a third study at a second municipality where we found our results were consistent. We then discuss design implications of these tasks because the task model has implications for 1) how information should be modelled, and 2) how information can be presented in context, and it provides implicit suggestions for 3) how users want to interact with information. Our work is a step towards better understanding the search requirements of target user groups within an organisation. A task model enables organisations developing search systems to better prioritise where they should invest in new technology.
In this article, we propose a Latent Dirichlet Allocation– (LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user’s search intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user’s search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user’s search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models.
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El presente estudio evalúa la calidad de los materiales educativos en salud dirigidos a los usuarios/as migrantes, facilitados por organismos públicos, y disponibles en internet, con el fin de favorecer los autocuidados respecto a su salud, mediante un estudio observacional descriptivo, en el que se identifican páginas web de salud de organismos oficiales para población inmigrante, mediante el uso del motor de búsqueda Google. Tras analizarse las características interactivas de las webs seleccionadas, y del total de documentos disponibles, se evalúa la legibilidad del contenido con el analizador MU y la comprensión y usabilidad con la Herramienta de Evaluación de Materiales de Educación del Paciente (PEMAT), por observadores. Como resultados se obtiene que las páginas webs de organismos oficiales para inmigrantes, son precisas, con credibilidad, avaladas por fuentes y accesibles, pero dichas webs y el material del que disponían no estaban actualizados. La media de legibilidad de los documentos en castellano fue de 49,10 (DT=6,61); para los documentos en francés de 51,87 (DT=8,58) y los documentos en inglés 64,57 (DT=8,73). La puntuación promedio de comprensión PEMAT fue de 68,69 (DT=16,95) y de capacidad de acción 54,41 (DT= 24,26). Se concluye que los materiales educativos en salud no alcanzaron una calidad óptima. El nivel de lectura está por encima del promedio de un adulto, por lo que hay que hacer esfuerzos para mejorar la comprensión y usabilidad de los textos, así como contar con el público diana para la elaboración de materiales de educación sanitaria.
Conference Paper
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Although interactive query reformulation has been actively studied in the laboratory, little is known about the actual behavior of web searchers who are offered terminological feedback along with their search results. We analyze log sessions for two groups of users interacting with variants of the AltaVista search engine - a baseline group given no terminological feedback and a feedback group to whom twelve refinement terms are offered along with the search results. We examine uptake, refinement effectiveness, conditions of use, and refinement type preferences. Although our measure of overall session "success" shows no difference between outcomes for the two groups, we find evidence that a subset of those users presented with terminological feedback do make effective use of it on a continuing basis.
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Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.
First, a new model of searching in online and other information systems, called 'berrypicking', is discussed. This model, it is argued, is much closer to the real behavior of information searchers than the traditional model of information retrieval is, and, consequently, will guide our thinking better in the design of effective interfaces. Second, the research literature of manual information seeking behavior is drawn on for suggestions of capabilities that users might like to have in online systems, Third, based on the new model and the research on information seeking, suggestions are made for how new search capabilities could be incorporated into the design of search interfaces. Particular attention is given to the nature and types of browsing that can be facilitated.
In ‘ASK for Information Retrieval: Part I’, we discussed the theory and background to a design study for an information retrieval (IR) system based on the attempt to represent the anomalous states of knowledge (ASKs) underlying information needs. In Part II, we report the methods and results of the design study, and our conclusions.
First, a new model of searching in online and other information systems, called 'berrypicking', is discussed. This model, it is argued, is much closer to the real behavior of information searchers than the traditional model of information retrieval is, and, consequently, will guide our thinking better in the design of effective interfaces. Second, the research literature of manual information seeking behavior is drawn on for suggestions of capabilities that users might like to have in online systems. Third, based on the new model and the research on information seeking, suggestions are made for how new search capabilities could be incorporated into the design of search interfaces. Particular attention is given to the nature and types of browsing that can be facilitated.
As part of the study of human information search strategy, the concept of the search tactic, or move made to further a search, is introduced. Twenty-nine tactics are named, defined, and discussed in four categories: monitoring, file structure, search formulation, and term. Implications of the search tactics for research in search strategy are considered. The search tactics are intended to be practically useful in information searching. This approach to searching is designed to be general, yet nontrivial; it is applicable to both bibliographic and reference searches and in both manual and on-line systems.
In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such.
User interfaces of Web search engines reflect attributes of the underlying tools used to create them, rather than what we know about how people look for information. In this article, the author examines several characteristics of user search behavior: the variety of information-seeking goals, the cultural and situational context of search, and the iterative nature of the search task. An analysis of these characteristics suggests ways that interfaces can be redesigned to make searching more effective for users. © 2006 Wiley Periodicals, Inc.
Research on Web searching is at an incipient stage. This aspect provides a unique opportunity to review the current state of research in the field, identify common trends, develop a methodological framework, and define terminology for future Web searching studies. In this article, the results from published studies of Web searching are reviewed to present the current state of research. The analysis of the limited Web searching studies available indicates that research methods and terminology are already diverging. A framework is proposed for future studies that will facilitate comparison of results. The advantages of such a framework are presented, and the implications for the design of Web information retrieval systems studies are discussed. Additionally, the searching characteristics of Web users are compared and contrasted with users of traditional information retrieval and online public access systems to discover if there is a need for more studies that focus predominantly or exclusively on Web searching. The comparison indicates that Web searching differs from searching in other environments.