Search Engines Evaluation (original) (raw)
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Performance Evaluation of Selected Search Engines on the Internet
2008
Search Engines have become an integral part of daily internet usage. The search engine is the first stop for web users when they are looking for a product. Information retrieval may be viewed as a problem of classifying items into one of two classes corresponding to interesting and uninteresting items respectively. A natural performance metric in this context is classification accuracy, defined as the fraction of the system's interesting/uninteresting predictions that agree with the user's assessments. On the other hand, the field of information retrieval has two classical performance evaluation metrics: precision, the fraction of the items retrieved by the system that are interesting to the user, and recall, the fraction of the items of interest to the user that are retrieved by the system. Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of human relevance judgments involved. However, both for business enterprises and people it is important to know the most effective Web search engines, since such search engines help their users find higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. This study evaluates the performance of three Web search engines. A set of measurements is proposed for evaluating Web search engine performance.
IOSR Journal of Engineering (IOSRJEN) Performance Evaluation of Selected Search Engines
Search Engines have become an integral part of daily internet usage. The search engine is the first stop for web users when they are looking for a product. Information retrieval may be viewed as a problem of classifying items into one of two classes corresponding to interesting and uninteresting items respectively. A natural performance metric in this context is classification accuracy, defined as the fraction of the system's interesting/uninteresting predictions that agree with the user's assessments. On the other hand, the field of information retrieval has two classical performance evaluation metrics: precision, the fraction of the items retrieved by the system that are interesting to the user, and recall, the fraction of the items of interest to the user that are retrieved by the system. Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of human relevance judgments involved. However, both for business enterprises and people it is important to know the most effective Web search engines, since such search engines help their users find higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. This study evaluates the performance of three Web search engines. A set of measurements is proposed for evaluating Web search engine performance.
Towards information retrieval measures for evaluation of Web search engines
Unpublished manuscript, 1999
Information retrieval on the Web is very different from retrieval in traditional indexed databases. This difference arises from: the high degree of dynamism of the Web; its hyper-linked character; the absence of a controlled indexing vocabulary; the heterogeneity of document types and authoring styles; the easy access that different types of users may have to it. Thus, since Web retrieval is substantially different from information retrieval, new or revised evaluative measures are required to assess retrieval performance using Web search engines. This paper suggests a number of different measures to evaluate information retrieval from the Web. The motivation behind each of these measures is presented, along with their descriptions and definitions. In the second part of the paper, application of these measures is illustrated in the evaluation of three search engines. The purpose of this paper is not to give the definite prescription for evaluating information retrieval from the Web, but rather to present some examples and to initiate a wider discussion of how to enhance measures of Web search performance.
Automatic performance evaluation of Web search engines
Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of human relevance judgments involved. However, both for business enterprises and people it is important to know the most effective Web search engines, since such search engines help their users find higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. In this study we introduce automatic Web search engine evaluation method as an efficient and effective assessment tool of such systems. The experiments based on eight Web search engines, 25 queries, and binary user relevance judgments show that our method provides results consistent with human-based evaluations. It is shown that the observed consistencies are statistically significant. This indicates that the new method can be successfully used in the evaluation of Web search engines.
Some Empirical Research on the Performance of Internet Search Engines
In this paper the IRT project (Internet / Information Retrieval Tools) is described. The basic goal of IRT is to advise users of Internet search engines in retrieving information from the free public access part of the Internet. In achieving this, IRT has developed a model to evaluate search engines. This model is described in here. Evaluation criteria refer to functionality: search options, presentation characteristics and indexing characteristics (which elements of a Web document are indexed?). Also evaluated is the consistency of retrieval through search engines. This model has been tested in the period October-December 1998 on six of the major search engines. We found many differences among Internet indexes in their functionality, as well as in their consistency and reliability.
Measuring Search Engine Quality
2001
The effectiveness of twenty public search engines is evaluated using TREC-inspired methods and a set of 54 queries taken from real Web search logs. The World Wide Web is taken as the test collection and a combination of crawler and text retrieval system is evaluated. The engines are compared on a range of measures derivable from binary relevance judgments of the first seven live results returned. Statistical testing reveals a significant difference between engines and high intercorrelations between measures. Surprisingly, given the dynamic nature of the Web and the time elapsed, there is also a high correlation between results of this study and a previous study by Gordon and Pathak. For nearly all engines, there is a gradual decline in precision at increasing cutoff after some initial fluctuation. Performance of the engines as a group is found to be inferior to the group of participants in the TREC-8 Large Web task, although the best engines approach the median of those systems. Shortcomings of current Web search evaluation methodology are identified and recommendations are made for future improvements. In particular, the present study and its predecessors deal with queries which are assumed to derive from a need to find a selection of documents relevant to a topic. By contrast, real Web search reflects a range of other information need types which require different judging and different measures.
EVALUATION AND PERFORMANCE OF WORLD WIDE WEB SEARCH ENGINES: A COMPARATIVE STUDY
The World Wide Web has revolutionized the way the people access information, and has opened up new possibilities in areas such as digi tal libraries, information dissemination and retrieval, education, commerce, entertainment, government and health care. The amount of publicly available information on the web is increasing consistently at an unbelievable rate. The web is a gigantic digital library, a searchable 15 billionword encyclopedia. It has stimulated res earch and development in information retrieval and dissemination. The revolution that the web has brought to information access is not so much due to the availability of information (huge amounts of i nformation has long been available in librari es), but rather the increased efficiency of accessing i nformation, which can make previously impractical tasks practical.
A Comparative Analysis of Search Engine Ranking Algorithms
International Journal of Advanced Trends in Computer Science and Engineering, 2021
Ranking Algorithm is the most proper way of positioning on a scale. As the information and knowledge on the internet are increasing every day.The search engine's ability to deliver the most appropriate material to the customer. It is more and more challenging without even any assistance in filtering through all of it. However, searching what user requires is extremely difficult. In this research, an effort has been made to compare and analyze the most popular and effective search engines. The keywords were used in uniform resource locator like, title tag, header, or even the keyword's resembles to the actual text. The page rank algorithm computes a perfect judgment of how relevant a webpage is by analyzing the quality and calculating the number of links connected to it. In this study the keyword relevancy and time response were used for search engines and observed the results. It is observed that the google search engine is faster than the bing and youtube, and after all, bing is the best search engine after google. Moreover, youtube is the fastest search engine in terms of video content search. The google results were found more accurate. However, it is better than all of the search engines.
A Survey on Performance Evaluation Measures for Information Retrieval System
2015
information to the users. To make the search effective, a tool called search engine has been introduced. These engines crawl the web for the given users query and display the results to the user based on the relevance score (ranking). Different search engine employs different ranking algorithm. Many ranking algorithm is being introduced frequently by several researchers. Several metrics are available to assess the quality of the ranked web pages. This paper presents a survey on different evaluation measures that are available for information retrieval systems and search engines. Several illustrations are provided for all these metrics.
2021
Due to the presence of a massive range of internet sites, the search engine includes a crucial job of providing the relevant pages to the user, Search Engines like Google, use Page Ranking algorithmic program to rank sites in keeping with the standard of their content and their presence over the planet wide internet. programme improvement could be a method of accelerating the probabilities of a webpage to seem within the 1st page of the search result. Since, whenever the buyer searches for info, they supply a specific phrase or a keyword rather than the entire internet address, then the search engine use that keyword to seek out the relevant sites and show it in a very list with the foremost relevant page at the highest. So, a company might use programme improvement techniques to achieve up to its potential client by showing at the highest of the search results. during this paper, we'll be classifying and reviewing totally different technologies for search engine improvement supported their importance and their usage.