Search results

Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some people may be looking for specific web sites or may wish to conduct transactions with web services. This paper aims to focus on automatically classifying the different user intents behind web queries.

For the research reported in this paper, 130,000 web search engine queries are categorized as informational, navigational, or transactional using a k‐means clustering approach based on a variety of query traits.

The research findings show that more than 75 percent of web queries (clustered into eight classifications) are informational in nature, with about 12 percent each for navigational and transactional. Results also show that web queries fall into eight clusters, six primarily informational, and one each of primarily transactional and navigational.

This study provides an important contribution to web search literature because it provides information about the goals of searchers and a method for automatically classifying the intents of the user queries. Automatic classification of user intent can lead to improved web search engines by tailoring results to specific user needs.

The paper discusses how web search engines can use automatically classified user queries to provide more targeted and relevant results in web searching by implementing a real time classification method as presented in this research.

This research investigates a new application of a method for automatically classifying the intent of user queries. There has been limited research to date on automatically classifying the user intent of web queries, even though the pay‐off for web search engines can be quite beneficial.

The purpose of this paper is to highlight the retrieval effectiveness of search engines taking into consideration both precision and relative recall.

The study is based on search engines that are selected on the basis of Alexa (Actionable Analytics for the web) Rank. Alexa listed top 500 sites, namely, search engines, portals, directories, social networking sites, networking tools, etc. But the scope of study is confined to only general search engines on the basis of language which was confined to English. Therefore only two general search engines are selected for the study . Alexa reports Google.com as the most visited website worldwide and Yahoo.com as the fourth most visited website globally. A total of 15 queries were selected randomly from PG students of Department of Library and Information Science during a period of eight days (from May 8 to May 15, 2014) which are classified manually into navigational, informational and transactional queries. However, queries are largely distributed on the two selected search engines to check their retrieval effectiveness as a training data set in order to define some characteristics of each type. Each query was submitted to the selected search engines which retrieved a large number of results but only the first 30 results were evaluated to limit the study in view of the fact that most of the users usually look up under the first hits of a query.

The study estimated the precision and relative recall of Google and Yahoo. Queries using concepts in the field of Library and Information Science were tested and were divided into navigational queries, informational queries and transactional queries. Results of the study showed that the mean precision of Google was high with (1.10) followed by Yahoo with (0.88). While as, mean relative recall of Google was high with (0.68) followed by Yahoo with (0.31), respectively.

The study highlights the retrieval effectiveness of only two search engines.

The research work is authentic and does not contain any plagiarized work.

The purpose of this paper is to test major web search engines on their performance on navigational queries, i.e. searches for homepages.

In total, 100 user queries are posed to six search engines (Google, Yahoo!, MSN, Ask, Seekport, and Exalead). Users described the desired pages, and the results position of these was recorded. Measured success and mean reciprocal rank are calculated.

The performance of the major search engines Google, Yahoo!, and MSN was found to be the best, with around 90 per cent of queries answered correctly. Ask and Exalead performed worse but received good scores as well.

All queries were in German, and the German‐language interfaces of the search engines were used. Therefore, the results are only valid for German queries.

When designing a search engine to compete with the major search engines, care should be taken on the performance on navigational queries. Users can be influenced easily in their quality ratings of search engines based on this performance.

This study systematically compares the major search engines on navigational queries and compares the findings with studies on the retrieval effectiveness of the engines on informational queries.

The purpose of this paper is to compare five major web search engines (Google, Yahoo, MSN, Ask.com, and Seekport) for their retrieval effectiveness, taking into account not only the results, but also the results descriptions.

The study uses real‐life queries. Results are made anonymous and are randomized. Results are judged by the persons posing the original queries.

The two major search engines, Google and Yahoo, perform best, and there are no significant differences between them. Google delivers significantly more relevant result descriptions than any other search engine. This could be one reason for users perceiving this engine as superior.

The study is based on a user model where the user takes into account a certain amount of results rather systematically. This may not be the case in real life.

The paper implies that search engines should focus on relevant descriptions. Searchers are advised to use other search engines in addition to Google.

This is the first major study comparing results and descriptions systematically and proposes new retrieval measures to take into account results descriptions.

The purpose of this paper is to investigate when and how people working in collaboration could be benefitted by an exploratory search task, specifically focussing on team size and its effect on the outcomes of such a task.

The paper investigates the effects of team sizes on exploratory search tasks using a lab study involving 68 participants – 12 individuals, ten dyads, and 12 triads. In order to assess various factors during their exploratory search sessions, an evaluation framework is synthesized using relevant literature. The framework consists of measures for five groups of quantities relevant to exploratory search: information exposure, information relevancy, information search, performance, and learning.

The analyses on the user study data using the proposed framework reveals that while individuals working alone cover more information than those working in teams, the teams (dyads and triads) are able to achieve better information coverage and search performance due to their collaborative strategies. In many of the measures, the triads are found to be even better than the dyads, demonstrating the value of adding a collaborator to a search process with multiple facets.

The findings shed light on not only how collaborative work could help in achieving better results in exploratory search, but also how team sizes affect specific aspects – information exposure, information relevancy, information search, performance, and learning – of exploratory search. This has implications for system designers, information managers, and educators.

The purpose of this paper is to investigate when and how people working collaboratively could be assisted in a fact-finding task, specifically focusing on team size and its effect on the outcomes of such a task. This is a follow-up to a previously published study that examined exploratory search tasks.

This research investigates the effects of team size on fact-finding tasks using a lab study involving 68 participants – 12 individuals, ten dyads, and 12 triads. The evaluation framework developed in the preceding work is used to compare the findings with respect to the earlier traditional exploratory task (Task 1) and the complex fact-finding task reported here (Task 2), with task type being the only difference.

The analyses of the user study data show that while adding more people to an exploratory search task could be beneficial in terms of efficiency and effectiveness, such findings do not apply in a complex fact-finding task. Indeed, results showed that the individuals were more efficient and effective doing Task 2 than they were in Task 1. Moreover, they outperformed the dyads and triads in Task 2 with respect to these two measures, which relate to the coverage of useful information and its relation to the expression of information needs. If the total time taken by each team is disregarded, the dyads and triads did better than the individuals in answering the fact-finding questions. But considering the time effect, this performance boost does not keep up with the increased group size.

The findings shed light not only on when, how, and why certain collaborations become successful, but also how team size affects specific aspects of information seeking, including information exposure, information relevancy, information search, and performance. This has implications for system designers, information managers, and educators. The presented work is novel in that it is the first empirical work to show the difference in individual and collaborative work (by dyads and triads) between exploratory and fact-finding tasks.

This paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services.

In recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services.

Three major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described.

This paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

The purpose of this paper is to describe the aspects to be considered when evaluating web search engines' accessibility for people with disabilities. The authors provide an overview of related work and outline a theoretical framework for a comprehensive accessibility study of web search engines, regarding the principles of disability studies and the idea of inclusion.

The paper is based on a literature review, and an aggregation of recommended actions in practice, mainly the W3C Web Accessibility Initiative's (WAI) evaluation model.

A good way to conduct an accessibility study in a comprehensive manner is the WAI methodology consisting of three‐steps: preliminary review to quickly identify potential accessibility problems; conformance evaluation to determine whether a website meets established accessibility standards; and user testing to include real people with disabilities in a practical use. For the use case “web search engines” some special issues have to be taken into consideration.

The paper can be seen as a brainstorming and describes a theoretical concept of how to do. Conclusions about actual barriers of web search engines and criteria of satisfaction for people with disabilities do not exist as of yet; the model is not tested so far.

This paper provides practical implications for researchers who want to conduct an accessibility study, especially of web search engines. Findings of such studies can have practical implications for web search engine developers to improve accessibility of their product. The accessibility of web search engines does not only have implications for people with special needs, but also for the elderly or temporarily handicapped people.

This paper combines findings from web search engine research with aspects of disability studies. Therefore, it provides insights for researches, search engine developers and educators in practice on how important accessibility of web search engines for people with disabilities is, how it can be measured and what aspects need to be considered.