6,515 research outputs found
PRESY: A Context Based Query Reformulation Tool for Information Retrieval on the Web
Problem Statement: The huge number of information on the web as well as the
growth of new inexperienced users creates new challenges for information
retrieval. It has become increasingly difficult for these users to find
relevant documents that satisfy their individual needs. Certainly the current
search engines (such as Google, Bing and Yahoo) offer an efficient way to
browse the web content. However, the result quality is highly based on uses
queries which need to be more precise to find relevant documents. This task
still complicated for the majority of inept users who cannot express their
needs with significant words in the query. For that reason, we believe that a
reformulation of the initial user's query can be a good alternative to improve
the information selectivity. This study proposes a novel approach and presents
a prototype system called PRESY (Profile-based REformulation SYstem) for
information retrieval on the web. Approach: It uses an incremental approach to
categorize users by constructing a contextual base. The latter is composed of
two types of context (static and dynamic) obtained using the users' profiles.
The architecture proposed was implemented using .Net environment to perform
queries reformulating tests. Results: The experiments gives at the end of this
article show that the precision of the returned content is effectively
improved. The tests were performed with the most popular searching engine (i.e.
Google, Bind and Yahoo) selected in particular for their high selectivity.
Among the given results, we found that query reformulation improve the first
three results by 10.7% and 11.7% of the next seven returned elements. So as we
can see the reformulation of users' initial queries improves the pertinence of
returned content.Comment: 8 page
Why People Search for Images using Web Search Engines
What are the intents or goals behind human interactions with image search
engines? Knowing why people search for images is of major concern to Web image
search engines because user satisfaction may vary as intent varies. Previous
analyses of image search behavior have mostly been query-based, focusing on
what images people search for, rather than intent-based, that is, why people
search for images. To date, there is no thorough investigation of how different
image search intents affect users' search behavior.
In this paper, we address the following questions: (1)Why do people search
for images in text-based Web image search systems? (2)How does image search
behavior change with user intent? (3)Can we predict user intent effectively
from interactions during the early stages of a search session? To this end, we
conduct both a lab-based user study and a commercial search log analysis.
We show that user intents in image search can be grouped into three classes:
Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals
different user behavior patterns under these three intents, such as first click
time, query reformulation, dwell time and mouse movement on the result page.
Based on user interaction features during the early stages of an image search
session, that is, before mouse scroll, we develop an intent classifier that is
able to achieve promising results for classifying intents into our three intent
classes. Given that all features can be obtained online and unobtrusively, the
predicted intents can provide guidance for choosing ranking methods immediately
after scrolling
Towards an automated query modification assistant
Users who need several queries before finding what they need can benefit from
an automatic search assistant that provides feedback on their query
modification strategies. We present a method to learn from a search log which
types of query modifications have and have not been effective in the past. The
method analyses query modifications along two dimensions: a traditional
term-based dimension and a semantic dimension, for which queries are enriches
with linked data entities. Applying the method to the search logs of two search
engines, we identify six opportunities for a query modification assistant to
improve search: modification strategies that are commonly used, but that often
do not lead to satisfactory results.Comment: 1st International Workshop on Usage Analysis and the Web of Data
(USEWOD2011) in the 20th International World Wide Web Conference (WWW2011),
Hyderabad, India, March 28th, 201
Simulation of within-session query variations using a text segmentation approach
We propose a generative model for automatic query refor-
mulations from an initial query using the underlying subtopic structure of top ranked retrieved documents. We address three types of query reformulations a) specialization; b) generalization; and c) drift. To test
our model we generate the three reformulation variants starting with selected fields from the TREC-8 topics as the initial queries. We use manual judgments from multiple assessors to calculate the accuracy of the reformulated query variants and observe accuracies of 65%, 82% and 69%
respectively for specialization, generalization and drift reformulations
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