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Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Source
- Information processing and management. 22(1986) no.6, S.465-476
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- Date
- 30. 3.2001 13:32:22
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Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
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- Date
- 25. 8.2005 17:42:22
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Cole, C.: Intelligent information retrieval: diagnosing information need : Part I: the theoretical framework for developing an intelligent IR tool (1998)
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Wills, R.S.: Google's PageRank : the math behind the search engine (2006)
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- Abstract
- Approximately 91 million American adults use the Internet on a typical day The number-one Internet activity is reading and writing e-mail. Search engine use is next in line and continues to increase in popularity. In fact, survey findings indicate that nearly 60 million American adults use search engines on a given day. Even though there are many Internet search engines, Google, Yahoo!, and MSN receive over 81% of all search requests. Despite claims that the quality of search provided by Yahoo! and MSN now equals that of Google, Google continues to thrive as the search engine of choice, receiving over 46% of all search requests, nearly double the volume of Yahoo! and over four times that of MSN. I use Google's search engine on a daily basis and rarely request information from other search engines. One day, I decided to visit the homepages of Google. Yahoo!, and MSN to compare the quality of search results. Coffee was on my mind that day, so I entered the simple query "coffee" in the search box at each homepage. Table 1 shows the top ten (unsponsored) results returned by each search engine. Although ordered differently, two webpages, www.peets.com and www.coffeegeek.com, appear in all three top ten lists. In addition, each pairing of top ten lists has two additional results in common. Depending on the information I hoped to obtain about coffee by using the search engines, I could argue that any one of the three returned better results: however, I was not looking for a particular webpage, so all three listings of search results seemed of equal quality. Thus, I plan to continue using Google. My decision is indicative of the problem Yahoo!, MSN, and other search engine companies face in the quest to obtain a larger percentage of Internet search volume. Search engine users are loyal to one or a few search engines and are generally happy with search results. Thus, as long as Google continues to provide results deemed high in quality, Google likely will remain the top search engine. But what set Google apart from its competitors in the first place? The answer is PageRank. In this article I explain this simple mathematical algorithm that revolutionized Web search.
-
Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
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- 14. 6.2015 22:12:44
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Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986)
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- 14. 6.2015 22:12:56
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Ruthven, I.; Lalmas, M.: Selective relevance feedback using term characteristics (1999)
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-
Lalmas, M.; Ruthven, I.: Representing and retrieving structured documents using the Dempster-Shafer theory of evidence : modelling and evaluation (1998)
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-
Na, S.-H.; Kang, I.-S.; Roh, J.-E.; Lee, J.-H.: ¬An empirical study of query expansion and cluster-based retrieval in language modeling approach (2007)
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Abdelkareem, M.A.A.: In terms of publication index, what indicator is the best for researchers indexing, Google Scholar, Scopus, Clarivate or others? (2018)
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- Abstract
- I believe that Google Scholar is the most popular academic indexing way for researchers and citations. However, some other indexing institutions may be more professional than Google Scholar but not as popular as Google Scholar. Other indexing websites like Scopus and Clarivate are providing more statistical figures for scholars, institutions or even journals. On account of publication citations, always Google Scholar shows higher citations for a paper than other indexing websites since Google Scholar consider most of the publication platforms so he can easily count the citations. While other databases just consider the citations come from those journals that are already indexed in their database
-
MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004)
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- Date
- 20. 1.2007 18:30:22
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Faloutsos, C.: Signature files (1992)
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- Date
- 7. 5.1999 15:22:48
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Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003)
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- Date
- 22. 3.2003 19:27:23
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Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
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Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014)
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- 13. 9.2014 14:45:22
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Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
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- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
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Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998)
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- 1. 8.1996 22:08:06
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Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
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- 20. 3.2005 16:23:22
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White, R.W.; Jose, J.M.; Ruthven, I.: ¬An implicit feedback approach for interactive information retrieval (2006)
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