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On the Weakenesses of Correlation Measures used for Search Engines' Results (Unsupervised Comparison of Search Engine Rankings)  [PDF]
Paolo D'Alberto,Ali Dasdan
Computer Science , 2011,
Abstract: The correlation of the result lists provided by search engines is fundamental and it has deep and multidisciplinary ramifications. Here, we present automatic and unsupervised methods to assess whether or not search engines provide results that are comparable or correlated. We have two main contributions: First, we provide evidence that for more than 80% of the input queries - independently of their frequency - the two major search engines share only three or fewer URLs in their search results, leading to an increasing divergence. In this scenario (divergence), we show that even the most robust measures based on comparing lists is useless to apply; that is, the small contribution by too few common items will infer no confidence. Second, to overcome this problem, we propose the fist content-based measures - i.e., direct comparison of the contents from search results; these measures are based on the Jaccard ratio and distribution similarity measures (CDF measures). We show that they are orthogonal to each other (i.e., Jaccard and distribution) and extend the discriminative power w.r.t. list based measures. Our approach stems from the real need of comparing search-engine results, it is automatic from the query selection to the final evaluation and it apply to any geographical markets, thus designed to scale and to use as first filtering of query selection (necessary) for supervised methods.
Performance Evaluation of search engines via user effort measures  [PDF]
Rajesh Kumar Goutam,Sanjay K. Dwivedi
International Journal of Computer Science Issues , 2012,
Abstract: Many metrics exist to perform the task of search engine evaluation that are either looking for the experts judgments or believe in searchers decisions about the relevancy of the web documents. However, search logs can provide us information about how real users search. This paper explains, our attempts to incorporate the users searching behavior in formulation of user efforts centric evaluation metric. We also incorporate two dimensional users traversing approach in the ERR metric. After the formulation of the evaluation metric, authors judge its goodness and found that presented metric fulfills all the requirements that are needed for a metric to be mathematically accurate. The findings obtained from experiments, present a complete description for search engine evaluation procedure.
Credibility in Web Search Engines  [PDF]
Dirk Lewandowski
Computer Science , 2012,
Abstract: Web search engines apply a variety of ranking signals to achieve user satisfaction, i.e., results pages that provide the best-possible results to the user. While these ranking signals implicitly consider credibility (e.g., by measuring popularity), explicit measures of credibility are not applied. In this chapter, credibility in Web search engines is discussed in a broad context: credibility as a measure for including documents in a search engine's index, credibility as a ranking signal, credibility in the context of universal search results, and the possibility of using credibility as an explicit measure for ranking purposes. It is found that while search engines-at least to a certain extent-show credible results to their users, there is no fully integrated credibility framework for Web search engines.
Internet Search Engines
Fatmaa El Zahraa Mohamed Abdou
Cybrarians Journal , 2004,
Abstract: A general study about the internet search engines, the study deals main 7 points; the differance between search engines and search directories, components of search engines, the percentage of sites covered by search engines, cataloging of sites, the needed time for sites appearance in search engines, search capabilities, and types of search engines.
Multimedia Search Engines : Concept, Performance, and Types
Sayed Rabeh Sayed
Cybrarians Journal , 2005,
Abstract: A Research about multimedia search engines, it starts with definition of search engines at general and multimedia search engines, then explains how they work, and divided them into: Video search engines, Images search engines, and Audio search engines. Finally, it reviews a samples to multimedia search engines.
An Evaluation of Some Famous Chinese and English Search Engines

Bao DongmeiZhou Yueqing,

现代图书情报技术 , 2004,
Abstract: The article selects 10 most famous search engines (5 Chinese SEs and 5 English SEs), and from both aspects of quantitative and qualitative, the author measures the performance of the natural language searching on the ten search engines. The aim is to try to offer some theoretical and factual foundation for the further optimization and improvement of search engines.
The egalitarian effect of search engines  [PDF]
Santo Fortunato,Alessandro Flammini,Filippo Menczer,Alessandro Vespignani
Physics , 2005, DOI: 10.1073/pnas.0605525103
Abstract: Search engines have become key media for our scientific, economic, and social activities by enabling people to access information on the Web in spite of its size and complexity. On the down side, search engines bias the traffic of users according to their page-ranking strategies, and some have argued that they create a vicious cycle that amplifies the dominance of established and already popular sites. We show that, contrary to these prior claims and our own intuition, the use of search engines actually has an egalitarian effect. We reconcile theoretical arguments with empirical evidence showing that the combination of retrieval by search engines and search behavior by users mitigates the attraction of popular pages, directing more traffic toward less popular sites, even in comparison to what would be expected from users randomly surfing the Web.
IntelligentWeb Agent for Search Engines  [PDF]
Avinash N Bhute,B. B. Meshram
Computer Science , 2013,
Abstract: In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. Search engines are retrieve the efficient information. We collected data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. The trends cited by the sources are consistent and point to exponential growth in the past and in the coming decade. Hence it is not surprising that about 85% of Internet users surveyed claim using search engines and search services to find specific information and users are not satisfied with the performance of the current generation of search engines; the slow retrieval speed, communication delays, and poor quality of retrieved results. Web agents, programs acting autonomously on some task, are already present in the form of spiders, crawler, and robots. Agents offer substantial benefits and hazards, and because of this, their development must involve attention to technical details. This paper illustrates the different types of agents,crawlers, robots,etc for mining the contents of web in a methodical, automated manner, also discusses the use of crawler to gather specific types of information from Web pages, such as harvesting e-mail addresses
Editorial: Link Spam and Search Engines  [cached]
Alireza Noruzi
Webology , 2006,
Abstract: The growing number of blogs has caused problems for search engines, problems such as the highly frequent blog spam. Spammers use blogs to promote their websites. Spammers are trying to win the attention of search engines, not of bloggers or their readers. Link spam dishonestly and deliberately manipulates link-based ranking algorithms of search engines like Google's PageRank to increase the rank of a web site or page so that it is placed as close to the top of search results as possible. A link-based ranking algorithm gives a higher ranking to a site that has many backlinks, especially from highly-ranked sites/pages.
The Retrieval Effectiveness of Web Search Engines: Considering Results Descriptions  [PDF]
Dirk Lewandowski
Computer Science , 2015,
Abstract: Purpose: 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. Design/Methodology/Approach: The study uses real-life queries. Results are made anonymous and are randomised. Results are judged by the persons posing the original queries. Findings: 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. Research Limitations: 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. Practical Implications: Implies that search engines should focus on relevant descriptions. Searchers are advised to use other search engines in addition to Google. Originality/Value: This is the first major study comparing results and descriptions systematically and proposes new retrieval measures to take into account results descriptions
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