Search

A task-oriented, comparative evaluation between four Web retrieval systems was performed; two using query-biased summarization, and two using the standard ranked titles/abstracts approach. Results indicate that query-biased summarization techniques appear to be more useful and effective in helping users gauge document relevance than the traditional ranked titles/abstracts approach. The same methodology was used to compare effectiveness of two search engines, AltaVista and Google. Includes eight tables and two figures. (AEF)

In explicit collaborative search, two or more individuals coordinate their efforts toward a shared goal. Every day, Internet users with similar information needs have the potential to collaborate. However, online search is typically performed in solitude. Existing search systems do not promote explicit collaborations, and collaboration opportunities (collabportunities) are missed. In this article, we describe a method to evaluate the feasibility of transforming these collabportunities into recommendations for explicit collaboration. We developed a technique called pseudocollaboration to evaluate the benefits and costs of collabportunities through simulations. We evaluate the performance of our method using three data sets: (a) data from single users' search sessions, (b) data with collaborative search sessions between pairs of searchers, and (c) logs from a large-scale search engine with search sessions of thousands of searchers. Our results establish when and how collabportunities would significantly help or hinder the search process versus searches conducted individually. The method that we describe has implications for the design and implementation of recommendation systems for explicit collaboration. It also connects system-mediated and user-mediated collaborative search, whereby the system evaluates the likely benefits of collaborating for a search task and helps searchers make more informed decisions on initiating and executing such a collaboration. [ABSTRACT FROM AUTHOR]

Search engines help people answer consequential questions. Biases in retrieved and indexed content (e.g., skew toward erroneous outcomes that represent deviations from reality), coupled with searchers' biases in how they examine and interpret search results, can lead people to incorrect answers. In this article, we seek to better understand biases in search and retrieval, and in particular those affecting the accuracy of content in search results, including the search engine index, features used for ranking, and the formulation of search queries. Focusing on the important domain of online health search, this research broadens previous work on biases in search to examine the role of search systems in contributing to biases. To assess bias, we focus on questions about medical interventions and employ reliable ground truth data from authoritative medical sources. In the course of our study, we utilize large-scale log analysis using data from a popular Web search engine, deep probes of result lists on that search engine, and crowdsourced human judgments of search result captions and landing pages. Our findings reveal bias in results, amplifying searchers' existing biases that appear evident in their search activity. We also highlight significant bias in indexed content and show that specific ranking signals and specific query terms support bias. Both of these can degrade result accuracy and increase skewness in search results. Our analysis has implications for bias mitigation strategies in online search systems, and we offer recommendations for search providers based on our findings. [ABSTRACT FROM AUTHOR]

People frequently turn to the Web with the goal of diagnosing medical symptoms. Studies have shown that diagnostic search can often lead to anxiety about the possibility that symptoms are explained by the presence of rare, serious medical disorders, rather than far more common benign syndromes. We study the influence of the appearance of potentially-alarming content, such as severe illnesses or serious treatment options associated with the queried for symptoms, in captions comprising titles, snippets, and URLs. We explore whether users are drawn to results with potentially-alarming caption content, and if so, the implications of such attraction for the design of search engines. We specifically study the influence of the content of search result captions shown in response to symptom searches on search-result click-through behavior. We show that users are significantly more likely to examine and click on captions containing potentially-alarming medical terminology such as "heart attack" or "medical emergency" independent of result rank position and well-known positional biases in users' search examination behaviors. The findings provide insights about the possible effects of displaying implicit correlates of searchers' goals in search-result captions, such as unexpressed concerns and fears. As an illustration of the potential utility of these results, we developed and evaluated an enhanced click prediction model that incorporates potentially-alarming caption features and show that it significantly outperforms models that ignore caption content. Beyond providing additional understanding of the effects ofWeb content on medical concerns, the methods and findings have implications for search engine design. As part of our discussion on the implications of this research, we propose procedures for generating more representative captions that may be less likely to cause alarm, as well as methods for learning to more appropriately rank search results from logged search behavior, for examples, by also considering the presence of potentially-alarming content in the captions that motivate observed clicks and down-weighting clicks seemingly driven by searchers' health anxieties. [ABSTRACT FROM AUTHOR]

When users have poorly defined or complex goals, search interfaces that offer only keyword-searching facilities provide inadequate support to help them reach their information-seeking objectives. The emergence of interfaces with more advanced capabilities, such as faceted browsing and result clustering, can go some way toward addressing such problems. The evaluation of these interfaces, however, is challenging because they generally offer diverse and versatile search environments that introduce overwhelming amounts of independent variables to user studies; choosing the interface object as the only independent variable in a study would reveal very little about why one design outperforms another. Nonetheless, if we could effectively compare these interfaces, then we would have a way to determine which was best for a given scenario and begin to learn why. In this article, we present a formative inspection framework for the evaluation of advanced search interfaces through the quantification of the strengths and weaknesses of the interfaces in supporting user tactics and varying user conditions. This framework combines established models of users and their needs and behaviors to achieve this. The framework is applied to evaluate three search interfaces and demonstrates the potential value of this approach to interactive information retrieval evaluation. [ABSTRACT FROM AUTHOR]

Advances in search technology have meant that search systems can now offer assistance to users beyond simply retrieving a set of documents. For example, search systems are now capable of inferring user interests by observing their interaction, offering suggestions about what terms could be used in a query, or reorganizing search results to make exploration of retrieved material more effective. When providing new search functionality, system designers must decide how the new functionality should be offered to users. One major choice is between (a) offering automatic features that require little human input, but give little human control; or (b) interactive features which allow human control over how the feature is used, but often give little guidance over how the feature should be best used. This article presents a study in which we empirically investigate the issue of control by presenting an experiment in which participants were asked to interact with three experimental systems that vary the degree of control they had in creating queries, indicating which results are relevant in making search decisions. We use our findings to discuss why and how the control users want over search decisions can vary depending on the nature of the decisions and the impact of those decisions on the user's search. [ABSTRACT FROM AUTHOR]

The article discusses information-seeking support systems (ISSSs) and presents an introduction to content in the current issue. An ISSS goes beyond mere retrieval of information via Web searches to provide tools for managing, analyzing, and sharing search results. ISSS is particularly well-suited to long-term informational needs, such as those related to treating a chronic illness or investigating scientific phenomena. ISSS-related articles in this issue include one on how descriptive models are being replaced by predictive and rational ones for information-seeking, another that provides historical context for modern search tools, and a third on collaborative search.