5,361 research outputs found
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
Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining
Visitor interaction with e-commerce websites generates large amounts of clickstream data stored in web access logs. From a business standpoint, clickstream data can be used as a means of finding information on user interest. In this paper, the authors propose a method to find user interest in products offered on e-commerce websites based on web usage mining of clickstream data. In this study, user interest was investigated using the PIE approach coupled with clustering and classification techniques. The experimental results showed that the method is able to assist in analyzing visitor behavior and user interest in e-commerce products by identifying those products that prompt visitor interest
Web Browsing Behavior Analysis and Interactive Hypervideo
© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in, ACM Transactions on the Web, Vol. 7, No. 4, Article 20, Publication date: October 2013.http://doi.acm.org/ 10.1145/2529995.2529996[EN] Processing data on any sort of user interaction is well known to be cumbersome and mostly time consuming.
In order to assist researchers in easily inspecting fine-grained browsing data, current tools usually display
user interactions as mouse cursor tracks, a video-like visualization scheme. However, to date, traditional
online video inspection has not explored the full capabilities of hypermedia and interactive techniques.
In response to this need, we have developed SMT 2ǫ, a Web-based tracking system for analyzing browsing
behavior using feature-rich hypervideo visualizations. We compare our system to related work in academia
and the industry, showing that ours features unprecedented visualization capabilities. We also show that
SMT 2ǫ efficiently captures browsing data and is perceived by users to be both helpful and usable. A series of
prediction experiments illustrate that raw cursor data are accessible and can be easily handled, providing
evidence that the data can be used to construct and verify research hypotheses. Considering its limitations,
it is our hope that SMT 2ǫ will assist researchers, usability practitioners, and other professionals interested
in understanding how users browse the Web.This work was partially supported by the MIPRCV Consolider Ingenio 2010 program (CSD2007-00018) and the TIN2009-14103-C03-03 project. It is also supported by the 7th Framework Program of the European Commision (FP7/2007-13) under grant agreement No. 287576 (CasMaCat).Leiva Torres, LA.; Vivó Hernando, RA. (2013). Web Browsing Behavior Analysis and Interactive Hypervideo. ACM Transactions on the Web. 7(4):20:1-20:28. https://doi.org/10.1145/2529995.2529996S20:120:287
The Price of Privacy - An Evaluation of the Economic Value of Collecting Clickstream Data
The analysis of clickstream data facilitates the understanding and prediction of customer behavior in e-commerce. Companies can leverage such data to increase revenue. For customers and website users, on the other hand, the collection of behavioral data entails privacy invasion. The objective of the paper is to shed light on the trade-off between privacy and the business value of cus- tomer information. To that end, the authors review approaches to convert clickstream data into behavioral traits, which we call clickstream features, and propose a categorization of these features according to the potential threat they pose to user privacy. The authors then examine the extent to which different categories of clickstream features facilitate predictions of online user shopping pat- terns and approximate the marginal utility of using more privacy adverse information in behavioral prediction models. Thus, the paper links the literature on user privacy to that on e-commerce analytics and takes a step toward an economic analysis of privacy costs and benefits. In par- ticular, the results of empirical experimentation with large real-world e-commerce data suggest that the inclusion of short-term customer behavior based on session-related information leads to large gains in predictive accuracy and business performance, while storing and aggregating usage behavior over longer horizons has comparably less value
Intrusion Detection Using Mouse Dynamics
Compared to other behavioural biometrics, mouse dynamics is a less explored
area. General purpose data sets containing unrestricted mouse usage data are
usually not available. The Balabit data set was released in 2016 for a data
science competition, which against the few subjects, can be considered the
first adequate publicly available one. This paper presents a performance
evaluation study on this data set for impostor detection. The existence of very
short test sessions makes this data set challenging. Raw data were segmented
into mouse move, point and click and drag and drop types of mouse actions, then
several features were extracted. In contrast to keystroke dynamics, mouse data
is not sensitive, therefore it is possible to collect negative mouse dynamics
data and to use two-class classifiers for impostor detection. Both action- and
set of actions-based evaluations were performed. Set of actions-based
evaluation achieves 0.92 AUC on the test part of the data set. However, the
same type of evaluation conducted on the training part of the data set resulted
in maximal AUC (1) using only 13 actions. Drag and drop mouse actions proved to
be the best actions for impostor detection.Comment: Submitted to IET Biometrics on 23 May 201
Automatic Web Navigation Problem Detection Based on Client-Side Interaction Data
The current importance of digital competence makes it essential to enable people with disabilities to use digital devices and applications and to automatically adapt site interactions to their needs. Although most of the current adaptable solutions make use of predefined user profiles, automatic detection of user abilities and disabilities is the foundation for building adaptive systems. This work contributes to diminishing the digital divide for people with disabilities by detecting the web navigation problems of users with physical disabilities based on a two-step strategy. The system is based on web user interaction data collected by the RemoTest platform and a complete data mining process applied to the data. First, the device used for interaction is recognized, and then, the problems the user may be having while interacting with the computer are detected. Identification of the device being used and the problems being encountered will allow the most adequate adaptation to be deployed and thus make the navigation more accessible
Moving Usability Testing onto the Web
Abstract: In order to remotely obtain detailed usability data by tracking user behaviors
within a given web site, a server-based usability testing environment has been
created. Web pages are annotated in such a way that arbitrary user actions (such as
"mouse over link" or "click back button") can be selected for logging. In addition,
the system allows the experiment designer to interleave interactive questions into
the usability evaluation, which for instance could be triggered by a particular sequence
of actions. The system works in conjunction with clustering and visualization
algorithms that can be applied to the resulting log file data. A first version of
the system has been used successfully to carry out a web usability evaluation
Measuring the Use of the Active and Assisted Living Prototype CARIMO for Home Care Service Users: Evaluation Framework and Results
To address the challenges of aging societies, various information and communication technology (ICT)-based systems for older people have been developed in recent years. Currently, the evaluation of these so-called active and assisted living (AAL) systems usually focuses on the analyses of usability and acceptance, while some also assess their impact. Little is known about
the actual take-up of these assistive technologies. This paper presents a framework for measuring the take-up by analyzing the actual usage of AAL systems. This evaluation framework covers detailed information regarding the entire process including usage data logging, data preparation, and usage data analysis. We applied the framework on the AAL prototype CARIMO for measuring
its take-up during an eight-month field trial in Austria and Italy. The framework was designed to guide systematic, comparable, and reproducible usage data evaluation in the AAL field; however, the general applicability of the framework has yet to be validated
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