Search (59 results, page 2 of 3)

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Abstract

Search engines are very popular tools for collecting information from distributed resources. They provide not only search facilities, but they also offer directories for users to browse content divided into groups. In this paper, we've adopted an individual differences approach to explore user's attitudes towards various interface features provided by existing Web directories. Among a variety of individual differences, cognitive style is a particularly important characteristic that influences the effectiveness of information seeking. Empirical results indicate that users' cognitive styles influence their reactions to the organization of subject categories, presentation of the results, and screen layout. We developed a set of design guidelines an the basis of these results, and propose a flexible interface that adopts these guidelines to accommodate the preferences of different cognitive style groups.

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Abstract

This paper uses a mix of qualitative and quantitative methodology to analyse differences between Finnish and American web searchers (n=27 per country) in their choice of initial search strategies (direct address, subject directory and search engines) and their reasoning underlying these choices, with data gathered via a questionnaire. The paper looks at these differences for four types of questions with two variables: closed/open and predictable/unpredictable source of answer (n=16 questions per searcher; total n=864 questions). The paper found significant differences between the two groups' initial search strategies and for three of the four types of questions. The reasoning varied across countries and questions as well, with Finns mentioning fewer reasons although both groups mentioned in aggregate a total of 1,284 reasons in twenty-four reason categories. The reasoning indicated that both country groups considered not only question-related reasons but also source- and search-strategy related reasons in making their decision. The research raises questions about considering cultural differences in designing web search access mechanisms.

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Abstract

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.

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Abstract

It is widely believed that many queries submitted to search engines are inherently ambiguous (e.g., java and apple). However, few studies have tried to classify queries based on ambiguity and to answer "what the proportion of ambiguous queries is". This paper deals with these issues. First, we clarify the definition of ambiguous queries by constructing the taxonomy of queries from being ambiguous to specific. Second, we ask human annotators to manually classify queries. From manually labeled results, we observe that query ambiguity is to some extent predictable. Third, we propose a supervised learning approach to automatically identify ambiguous queries. Experimental results show that we can correctly identify 87% of labeled queries with the approach. Finally, by using our approach, we estimate that about 16% of queries in a real search log are ambiguous.

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Abstract

Many studies have demonstrated that people engage in a variety of different information behaviors when engaging in information seeking. However, standard information retrieval systems such as Web search engines continue to be designed to support mainly one such behavior, specified searching. This situation has led to suggestions that people would be better served by information retrieval systems which support different kinds of information-seeking strategies. This article reports on an experiment comparing the retrieval effectiveness of an integrated interactive information retrieval (IIR) system which adapts to support different information-seeking strategies with that of a standard baseline IIR system. The experiment, with 32 participants each searching on eight different topics, indicates that using the integrated IIR system resulted in significantly better user satisfaction with search results, significantly more effective interaction, and significantly better usability than that using the baseline system.

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Abstract

Children represent an increasing group of web users. Some of the key problems that hamper their search experience is their limited vocabulary, their difficulty in using the right keywords, and the inappropriateness of their general-purpose query suggestions. In this work, we propose a method that uses tags from social media to suggest queries related to children's topics. Concretely, we propose a simple yet effective approach to bias a random walk defined on a bipartite graph of web resources and tags through keywords that are more commonly used to describe resources for children. We evaluate our method using a large query log sample of queries submitted by children. We show that our method outperforms by a large margin the query suggestions of modern search engines and state-of-the art query suggestions based on random walks. We improve further the quality of the ranking by combining the score of the random walk with topical and language modeling features to emphasize even more the child-related aspects of the query suggestions.

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Abstract

Query classification is an important part of exploring the characteristics of web queries. Existing studies are mainly based on Broder's classification scheme and classify user queries into navigational, informational, and transactional categories according to users' information needs. In this article, we present a novel classification scheme from the perspective of queries' temporal patterns. Queries' temporal patterns are inherent time series patterns of the search volumes of queries that reflect the evolution of the popularity of a query over time. By analyzing the temporal patterns of queries, search engines can more deeply understand the users' search intents and thus improve performance. Furthermore, we extract three groups of features based on the queries' search volume time series and use a support vector machine (SVM) to automatically detect the temporal patterns of user queries. Extensive experiments on the Million Query Track data sets of the Text REtrieval Conference (TREC) demonstrate the effectiveness of our approach.

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Abstract

Subject content analysis of Web query terms is essential to understand Web searching interests. Such analysis includes exploring search topics and observing changes in their frequency distributions with time. To provide a basis for in-depth analysis of users' search interests on a larger scale, this article presents a query categorization approach to automatically classifying Web query terms into broad subject categories. Because a query is short in length and simple in structure, its intended subject(s) of search is difficult to judge. Our approach, therefore, combines the search processes of real-world search engines to obtain highly ranked Web documents based on each unknown query term. These documents are used to extract cooccurring terms and to create a feature set. An effective ranking function has also been developed to find the most appropriate categories. Three search engine logs in Taiwan were collected and tested. They contained over 5 million queries from different periods of time. The achieved performance is quite encouraging compared with that of human categorization. The experimental results demonstrate that the approach is efficient in dealing with large numbers of queries and adaptable to the dynamic Web environment. Through good integration of human and machine efforts, the frequency distributions of subject categories in response to changes in users' search interests can be systematically observed in real time. The approach has also shown potential for use in various information retrieval applications, and provides a basis for further Web searching studies.

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Abstract

The aim of the work described in this paper is to evaluate the influencing effects of query-biased summaries in web searching. For this purpose, a summarisation system has been developed, and a summary tailored to the user's query is generated automatically for each document retrieved. The system aims to provide both a better means of assessing document relevance than titles or abstracts typical of many web search result lists. Through visiting each result page at retrieval-time, the system provides the user with an idea of the current page content and thus deals with the dynamic nature of the web. To examine the effectiveness of this approach, a task-oriented, comparative evaluation between four different web retrieval systems was performed; two that use query-biased summarisation, and two that use the standard ranked titles/abstracts approach. The results from the evaluation indicate that query-biased summarisation 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 the effectiveness of two of the web's major search engines; AltaVista and Google.

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Abstract

Purpose - This paper aims to report findings from an exploratory study investigating the web interactions and technoliteracy of children in the early childhood years. Previous research has studied aspects of older children's technoliteracy and web searching; however, few studies have analyzed web search data from children younger than six years of age. Design/methodology/approach - The study explored the Google web searching and technoliteracy of young children who are enrolled in a "preparatory classroom" or kindergarten (the year before young children begin compulsory schooling in Queensland, Australia). Young children were video- and audio-taped while conducting Google web searches in the classroom. The data were qualitatively analysed to understand the young children's web search behaviour. Findings - The findings show that young children engage in complex web searches, including keyword searching and browsing, query formulation and reformulation, relevance judgments, successive searches, information multitasking and collaborative behaviours. The study results provide significant initial insights into young children's web searching and technoliteracy. Practical implications - The use of web search engines by young children is an important research area with implications for educators and web technologies developers. Originality/value - This is the first study of young children's interaction with a web search engine.

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Abstract

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model supported by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world.

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Abstract

In recent years, searching the web on mobile devices has become enormously popular. Because mobile devices have relatively small screens and show fewer search results, search behavior with mobile devices may be different from that with desktops or laptops. Therefore, examining these differences may suggest better, more efficient designs for mobile search engines. In this experiment, we use eye tracking to explore user behavior and performance. We analyze web searches with 2 task types on 2 differently sized screens: one for a desktop and the other for a mobile device. In addition, we examine the relationships between search performance and several search behaviors to allow further investigation of the differences engendered by the screens. We found that users have more difficulty extracting information from search results pages on the smaller screens, although they exhibit less eye movement as a result of an infrequent use of the scroll function. However, in terms of search performance, our findings suggest that there is no significant difference between the 2 screens in time spent on search results pages and the accuracy of finding answers. This suggests several possible ideas for the presentation design of search results pages on small devices.

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Abstract

In this study, we evaluated the ability of computational cognitive models of web-navigation like CoLiDeS and CoLiDeS+ to model i) user interactions with search engines and ii) individual differences in search behavior due to variations in cognitive factors such as aging. CoLiDeS and CoLiDeS+ were extended to predict user clicks on search engine result pages. Their performance was evaluated using actual behavioral data from an experiment in which 2 types of information search tasks (simple vs. difficult), were presented to younger and older participants. The results showed that the model predictions matched significantly better with the actual user behavior on difficult tasks compared to simple tasks and with younger participants compared to older participants, especially for difficult tasks. Also, the matches were significantly better with CoLiDeS+ compared to CoLiDeS, especially for difficult tasks. We conclude that the advanced capabilities of CoLiDeS+, such as incorporating contextual information and implementing backtracking strategies enable it to predict user behavior significantly better than CoLiDeS, especially on difficult tasks. The usefulness of these modeling outcomes for the design of support systems for older adults is discussed.

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Abstract

Exploratory searches involve significant cognitively demanding aiming at learning and investigation. However, users gain little support from search engines for their cognitive and metacognitive activities (e.g., discovery, synthesis, planning, transformation, monitoring, and reflection) during exploratory searches. To better support the exploratory search process, we designed a new search assistance tool called OrgBox. OrgBox allows users to drag-and-drop information they find during searches into "boxes" and "items" that can be created, labeled, and rearranged on a canvas. We conducted a controlled, within-subjects user study with 24 participants to evaluate the OrgBox versus a baseline tool called the OrgDoc that supported rich-text features. Our findings show that participants perceived the OrgBox tool to provide more support for grouping and reorganizing information, tracking thought processes, planning and monitoring search and task processes, and gaining a visual overview of the collected information. The usability test reveals users' preferences for simplicity, familiarity, and flexibility of the design of OrgBox, along with technical problems such as delay of response and restrictions of use. Our results have implications for the design of search-assisting systems that encourage cognitive and metacognitive activities during exploratory search processes.

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Abstract

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).

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Date

22. 5.2005 19:53:09

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Date

22. 5.2005 19:52:29

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Source

Library and information science research. 22(2000) no.4, S.371-382

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