22,401 research outputs found
Auditing Search Engines for Differential Satisfaction Across Demographics
Many online services, such as search engines, social media platforms, and
digital marketplaces, are advertised as being available to any user, regardless
of their age, gender, or other demographic factors. However, there are growing
concerns that these services may systematically underserve some groups of
users. In this paper, we present a framework for internally auditing such
services for differences in user satisfaction across demographic groups, using
search engines as a case study. We first explain the pitfalls of na\"ively
comparing the behavioral metrics that are commonly used to evaluate search
engines. We then propose three methods for measuring latent differences in user
satisfaction from observed differences in evaluation metrics. To develop these
methods, we drew on ideas from the causal inference literature and the
multilevel modeling literature. Our framework is broadly applicable to other
online services, and provides general insight into interpreting their
evaluation metrics.Comment: 8 pages Accepted at WWW 201
Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation Model
Modern search engine result pages often provide immediate value to users and
organize information in such a way that it is easy to navigate. The core
ranking function contributes to this and so do result snippets, smart
organization of result blocks and extensive use of one-box answers or side
panels. While they are useful to the user and help search engines to stand out,
such features present two big challenges for evaluation. First, the presence of
such elements on a search engine result page (SERP) may lead to the absence of
clicks, which is, however, not related to dissatisfaction, so-called "good
abandonments." Second, the non-linear layout and visual difference of SERP
items may lead to non-trivial patterns of user attention, which is not captured
by existing evaluation metrics.
In this paper we propose a model of user behavior on a SERP that jointly
captures click behavior, user attention and satisfaction, the CAS model, and
demonstrate that it gives more accurate predictions of user actions and
self-reported satisfaction than existing models based on clicks alone. We use
the CAS model to build a novel evaluation metric that can be applied to
non-linear SERP layouts and that can account for the utility that users obtain
directly on a SERP. We demonstrate that this metric shows better agreement with
user-reported satisfaction than conventional evaluation metrics.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on
Information and Knowledge Management. 201
Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management
An increase in users’ online searches, the social concern for an efficient management of resources such as water, and the appearance of more and more digital platforms for sustainable purposes to conduct online searches lead us to reflect more on the users’ behavioral intention with respect to search engines that support sustainable projects like water management projects. Another issue to consider is the factors that determine the adoption of such search engines. In the present study, we aim to identify the factors that determine the intention to adopt a search engine, such as Lilo, that favors sustainable water management. To this end, a model based on the Theory of Planned Behavior (TPB) is proposed. The methodology used is the Structural Equation Modeling (SEM) analysis with the Analysis of Moment Structures (AMOS). The results demonstrate that individuals who intend to use a search engine are influenced by hedonic motivations, which drive their feeling of contentment with the search. Similarly, the success of search engines is found to be closely related to the ability a search engine grants to its users to generate a social or environmental impact, rather than users’ trust in what they do or in their results. However, according to our results, habit is also an important factor that has both a direct and an indirect impact on users’ behavioral intention to adopt different search engines
Using the Global Web as an Expertise Evidence Source
This paper describes the details of our participation in expert search task of the TREC 2007 Enterprise track. The presented study demonstrates the predicting potential of the expertise evidence that can be found outside of
the organization. We discovered that combining the ranking built solely on the Enterprise data with the Global Web
based ranking may produce significant increases in performance. However, our main goal was to explore whether
this result can be further improved by using various quality measures to distinguish among web result items. While,
indeed, it was beneficial to use some of these measures, especially those measuring relevance of URL strings and titles,
it stayed unclear whether they are decisively important
University of Twente at the TREC 2008 Enterprise Track: using the Global Web as an expertise evidence source
This paper describes the details of our participation in expert search task of the TREC 2007 Enterprise track.\ud
This is the fourth (and the last) year of TREC 2007 Enterprise Track and the second year the University of Twente (Database group) submitted runs for the expert nding task. In the methods that were used to produce these runs, we mostly rely on the predicting potential of those expertise evidence sources that are publicly available on the Global Web, but not hosted at the website of the organization under study (CSIRO). This paper describes the follow-up studies\ud
complimentary to our recent research [8] that demonstrated how taking the web factor seriously signicantly improves the performance of expert nding in the enterprise
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