-
Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015)
0.03
0.031173116 = product of:
0.06234623 = sum of:
0.005885557 = product of:
0.023542227 = sum of:
0.023542227 = weight(_text_:based in 2696) [ClassicSimilarity], result of:
0.023542227 = score(doc=2696,freq=2.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.16644597 = fieldWeight in 2696, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.0390625 = fieldNorm(doc=2696)
0.25 = coord(1/4)
0.056460675 = weight(_text_:term in 2696) [ClassicSimilarity], result of:
0.056460675 = score(doc=2696,freq=2.0), product of:
0.21904005 = queryWeight, product of:
4.66603 = idf(docFreq=1130, maxDocs=44218)
0.04694356 = queryNorm
0.25776416 = fieldWeight in 2696, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.66603 = idf(docFreq=1130, maxDocs=44218)
0.0390625 = fieldNorm(doc=2696)
0.5 = coord(2/4)
- Abstract
- Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.
-
Lewandowski, D.: Evaluating the retrieval effectiveness of web search engines using a representative query sample (2015)
0.03
0.027245669 = product of:
0.054491337 = sum of:
0.0070626684 = product of:
0.028250674 = sum of:
0.028250674 = weight(_text_:based in 2157) [ClassicSimilarity], result of:
0.028250674 = score(doc=2157,freq=2.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.19973516 = fieldWeight in 2157, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.046875 = fieldNorm(doc=2157)
0.25 = coord(1/4)
0.047428668 = product of:
0.094857335 = sum of:
0.094857335 = weight(_text_:assessment in 2157) [ClassicSimilarity], result of:
0.094857335 = score(doc=2157,freq=2.0), product of:
0.25917634 = queryWeight, product of:
5.52102 = idf(docFreq=480, maxDocs=44218)
0.04694356 = queryNorm
0.36599535 = fieldWeight in 2157, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.52102 = idf(docFreq=480, maxDocs=44218)
0.046875 = fieldNorm(doc=2157)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- Search engine retrieval effectiveness studies are usually small scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, and data were collected using specialized software, the Relevance Assessment Tool (RAT). We found that although Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3% of cases, whereas Bing only found the correct answer 76.6% of the time. We conclude that search engine performance on navigational queries is of great importance, because users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines.
-
Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013)
0.02
0.017428853 = product of:
0.034857705 = sum of:
0.009416891 = product of:
0.037667565 = sum of:
0.037667565 = weight(_text_:based in 1149) [ClassicSimilarity], result of:
0.037667565 = score(doc=1149,freq=2.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.26631355 = fieldWeight in 1149, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.0625 = fieldNorm(doc=1149)
0.25 = coord(1/4)
0.025440816 = product of:
0.05088163 = sum of:
0.05088163 = weight(_text_:22 in 1149) [ClassicSimilarity], result of:
0.05088163 = score(doc=1149,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.30952093 = fieldWeight in 1149, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=1149)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- This paper presents a test of the validity of using Google Scholar to evaluate the publications of researchers by comparing the premises on which its search engine, PageRank, is based, to those of Garfield's theory of citation indexing. It finds that the premises are identical and that PageRank and Garfield's theory of citation indexing validate each other.
- Date
- 17.12.2013 11:02:22
-
Makris, C.; Plegas, Y.; Stamou, S.: Web query disambiguation using PageRank (2012)
0.02
0.016938202 = product of:
0.06775281 = sum of:
0.06775281 = weight(_text_:term in 378) [ClassicSimilarity], result of:
0.06775281 = score(doc=378,freq=2.0), product of:
0.21904005 = queryWeight, product of:
4.66603 = idf(docFreq=1130, maxDocs=44218)
0.04694356 = queryNorm
0.309317 = fieldWeight in 378, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.66603 = idf(docFreq=1130, maxDocs=44218)
0.046875 = fieldNorm(doc=378)
0.25 = coord(1/4)
- Abstract
- In this article, we propose new word sense disambiguation strategies for resolving the senses of polysemous query terms issued to Web search engines, and we explore the application of those strategies when used in a query expansion framework. The novelty of our approach lies in the exploitation of the Web page PageRank values as indicators of the significance the different senses of a term carry when employed in search queries. We also aim at scalable query sense resolution techniques that can be applied without loss of efficiency to large data sets such as those on the Web. Our experimental findings validate that the proposed techniques perform more accurately than do the traditional disambiguation strategies and improve the quality of the search results, when involved in query expansion.
-
Luo, M.M.; Nahl, D.: Let's Google : uncertainty and bilingual search (2019)
0.02
0.016938202 = product of:
0.06775281 = sum of:
0.06775281 = weight(_text_:term in 5363) [ClassicSimilarity], result of:
0.06775281 = score(doc=5363,freq=2.0), product of:
0.21904005 = queryWeight, product of:
4.66603 = idf(docFreq=1130, maxDocs=44218)
0.04694356 = queryNorm
0.309317 = fieldWeight in 5363, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.66603 = idf(docFreq=1130, maxDocs=44218)
0.046875 = fieldNorm(doc=5363)
0.25 = coord(1/4)
- Abstract
- This study applies Kuhlthau's Information Search Process stage (ISP) model to understand bilingual users' Internet search experience. We conduct a quasi-field experiment with 30 bilingual searchers and the results suggested that the ISP model was applicable in studying searchers' information retrieval behavior in search tasks. The ISP model was applicable in studying searchers' information retrieval behavior in simple tasks. However, searchers' emotional responses differed from those of the ISP model for a complex task. By testing searchers using different search strategies, the results suggested that search engines with multilanguage search functions provide an advantage for bilingual searchers in the Internet's multilingual environment. The findings showed that when searchers used a search engine as a tool for problem solving, they might experience different feelings in each ISP stage than in searching for information for a term paper using a library. The results echo other research findings that indicate that information seeking is a multifaceted phenomenon.
-
Lewandowski, D.; Sünkler, S.: ¬Das Relevance Assessment Tool : eine modulare Software zur Unterstützung bei der Durchführung vielfältiger Studien mit Suchmaschinen (2019)
0.02
0.01676857 = product of:
0.06707428 = sum of:
0.06707428 = product of:
0.13414855 = sum of:
0.13414855 = weight(_text_:assessment in 5026) [ClassicSimilarity], result of:
0.13414855 = score(doc=5026,freq=4.0), product of:
0.25917634 = queryWeight, product of:
5.52102 = idf(docFreq=480, maxDocs=44218)
0.04694356 = queryNorm
0.51759565 = fieldWeight in 5026, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
5.52102 = idf(docFreq=480, maxDocs=44218)
0.046875 = fieldNorm(doc=5026)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- In diesem Artikel stellen wir eine Software vor, mit der sich Studien zu Such- und Informationssystemen realisieren lassen. Das Relevance Assessment Tool (RAT) soll umfangreiche Untersuchungen mit Daten von kommerziellen Suchmaschinen unterstützen. Die Software ist modular und webbasiert. Es lassen sich damit automatisiert Daten von Suchmaschinen erfassen. Dazu können Studien mit Fragen und Skalen flexibel gestaltet und die Informationsobjekte anhand der Fragen durch Juroren bewertet werden. Durch die Modularität lassen sich die einzelnen Komponenten für eine Vielzahl von Studien nutzen, die sich auf Web-Inhalte beziehen. So kann die Software auch für qualitative Inhaltsanalysen eingesetzt werden oder durch das automatisierte Scraping eine große Datenbasis an Web-Dokumenten liefern, die sich quantitativ in empirischen Studien analysieren lassen.
-
Schaer, P.; Mayr, P.; Sünkler, S.; Lewandowski, D.: How relevant is the long tail? : a relevance assessment study on million short (2016)
0.01
0.013973806 = product of:
0.055895224 = sum of:
0.055895224 = product of:
0.11179045 = sum of:
0.11179045 = weight(_text_:assessment in 3144) [ClassicSimilarity], result of:
0.11179045 = score(doc=3144,freq=4.0), product of:
0.25917634 = queryWeight, product of:
5.52102 = idf(docFreq=480, maxDocs=44218)
0.04694356 = queryNorm
0.43132967 = fieldWeight in 3144, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
5.52102 = idf(docFreq=480, maxDocs=44218)
0.0390625 = fieldNorm(doc=3144)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.
-
Zhitomirsky-Geffet, M.; Bar-Ilan, J.; Levene, M.: Analysis of change in users' assessment of search results over time (2017)
0.01
0.013973806 = product of:
0.055895224 = sum of:
0.055895224 = product of:
0.11179045 = sum of:
0.11179045 = weight(_text_:assessment in 3593) [ClassicSimilarity], result of:
0.11179045 = score(doc=3593,freq=4.0), product of:
0.25917634 = queryWeight, product of:
5.52102 = idf(docFreq=480, maxDocs=44218)
0.04694356 = queryNorm
0.43132967 = fieldWeight in 3593, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
5.52102 = idf(docFreq=480, maxDocs=44218)
0.0390625 = fieldNorm(doc=3593)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- We present the first systematic study of the influence of time on user judgements for rankings and relevance grades of web search engine results. The goal of this study is to evaluate the change in user assessment of search results and explore how users' judgements change. To this end, we conducted a large-scale user study with 86 participants who evaluated 2 different queries and 4 diverse result sets twice with an interval of 2 months. To analyze the results we investigate whether 2 types of patterns of user behavior from the theory of categorical thinking hold for the case of evaluation of search results: (a) coarseness and (b) locality. To quantify these patterns we devised 2 new measures of change in user judgements and distinguish between local (when users swap between close ranks and relevance values) and nonlocal changes. Two types of judgements were considered in this study: (a) relevance on a 4-point scale, and (b) ranking on a 10-point scale without ties. We found that users tend to change their judgements of the results over time in about 50% of cases for relevance and in 85% of cases for ranking. However, the majority of these changes were local.
-
Chen, L.-C.: Next generation search engine for the result clustering technology (2012)
0.01
0.013071639 = product of:
0.026143279 = sum of:
0.0070626684 = product of:
0.028250674 = sum of:
0.028250674 = weight(_text_:based in 105) [ClassicSimilarity], result of:
0.028250674 = score(doc=105,freq=2.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.19973516 = fieldWeight in 105, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.046875 = fieldNorm(doc=105)
0.25 = coord(1/4)
0.019080611 = product of:
0.038161222 = sum of:
0.038161222 = weight(_text_:22 in 105) [ClassicSimilarity], result of:
0.038161222 = score(doc=105,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.23214069 = fieldWeight in 105, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=105)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- Result clustering has recently attracted a lot of attention to provide the users with a succinct overview of relevant search results than traditional search engines. This chapter proposes a mixed clustering method to organize all returned search results into a hierarchical tree structure. The clustering method accomplishes two main tasks, one is label construction and the other is tree building. This chapter uses precision to measure the quality of clustering results. According to the results of experiments, the author preliminarily concluded that the performance of the system is better than many other well-known commercial and academic systems. This chapter makes several contributions. First, it presents a high performance system based on the clustering method. Second, it develops a divisive hierarchical clustering algorithm to organize all returned snippets into hierarchical tree structure. Third, it performs a wide range of experimental analyses to show that almost all commercial systems are significantly better than most current academic systems.
- Date
- 17. 4.2012 15:22:11
-
Bouidghaghen, O.; Tamine, L.: Spatio-temporal based personalization for mobile search (2012)
0.01
0.013071639 = product of:
0.026143279 = sum of:
0.0070626684 = product of:
0.028250674 = sum of:
0.028250674 = weight(_text_:based in 108) [ClassicSimilarity], result of:
0.028250674 = score(doc=108,freq=2.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.19973516 = fieldWeight in 108, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.046875 = fieldNorm(doc=108)
0.25 = coord(1/4)
0.019080611 = product of:
0.038161222 = sum of:
0.038161222 = weight(_text_:22 in 108) [ClassicSimilarity], result of:
0.038161222 = score(doc=108,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.23214069 = fieldWeight in 108, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=108)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Date
- 20. 4.2012 13:19:22
-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
0.01
0.012111973 = product of:
0.024223946 = sum of:
0.008323434 = product of:
0.033293735 = sum of:
0.033293735 = weight(_text_:based in 1605) [ClassicSimilarity], result of:
0.033293735 = score(doc=1605,freq=4.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.23539014 = fieldWeight in 1605, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.0390625 = fieldNorm(doc=1605)
0.25 = coord(1/4)
0.015900511 = product of:
0.031801023 = sum of:
0.031801023 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
0.031801023 = score(doc=1605,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.19345059 = fieldWeight in 1605, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=1605)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
-
Alqaraleh, S.; Ramadan, O.; Salamah, M.: Efficient watcher based web crawler design (2015)
0.01
0.012111973 = product of:
0.024223946 = sum of:
0.008323434 = product of:
0.033293735 = sum of:
0.033293735 = weight(_text_:based in 1627) [ClassicSimilarity], result of:
0.033293735 = score(doc=1627,freq=4.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.23539014 = fieldWeight in 1627, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.0390625 = fieldNorm(doc=1627)
0.25 = coord(1/4)
0.015900511 = product of:
0.031801023 = sum of:
0.031801023 = weight(_text_:22 in 1627) [ClassicSimilarity], result of:
0.031801023 = score(doc=1627,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.19345059 = fieldWeight in 1627, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=1627)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- Purpose The purpose of this paper is to design a watcher-based crawler (WBC) that has the ability of crawling static and dynamic web sites, and can download only the updated and newly added web pages. Design/methodology/approach In the proposed WBC crawler, a watcher file, which can be uploaded to the web sites servers, prepares a report that contains the addresses of the updated and the newly added web pages. In addition, the WBC is split into five units, where each unit is responsible for performing a specific crawling process. Findings Several experiments have been conducted and it has been observed that the proposed WBC increases the number of uniquely visited static and dynamic web sites as compared with the existing crawling techniques. In addition, the proposed watcher file not only allows the crawlers to visit the updated and newly web pages, but also solves the crawlers overlapping and communication problems. Originality/value The proposed WBC performs all crawling processes in the sense that it detects all updated and newly added pages automatically without any human explicit intervention or downloading the entire web sites.
- Date
- 20. 1.2015 18:30:22
-
Lewandowski, D.; Sünkler, S.: What does Google recommend when you want to compare insurance offerings? (2019)
0.01
0.010893034 = product of:
0.021786068 = sum of:
0.005885557 = product of:
0.023542227 = sum of:
0.023542227 = weight(_text_:based in 5288) [ClassicSimilarity], result of:
0.023542227 = score(doc=5288,freq=2.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.16644597 = fieldWeight in 5288, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.0390625 = fieldNorm(doc=5288)
0.25 = coord(1/4)
0.015900511 = product of:
0.031801023 = sum of:
0.031801023 = weight(_text_:22 in 5288) [ClassicSimilarity], result of:
0.031801023 = score(doc=5288,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.19345059 = fieldWeight in 5288, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=5288)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- Purpose The purpose of this paper is to describe a new method to improve the analysis of search engine results by considering the provider level as well as the domain level. This approach is tested by conducting a study using queries on the topic of insurance comparisons. Design/methodology/approach The authors conducted an empirical study that analyses the results of search queries aimed at comparing insurance companies. The authors used a self-developed software system that automatically queries commercial search engines and automatically extracts the content of the returned result pages for further data analysis. The data analysis was carried out using the KNIME Analytics Platform. Findings Google's top search results are served by only a few providers that frequently appear in these results. The authors show that some providers operate several domains on the same topic and that these domains appear for the same queries in the result lists. Research limitations/implications The authors demonstrate the feasibility of this approach and draw conclusions for further investigations from the empirical study. However, the study is a limited use case based on a limited number of search queries. Originality/value The proposed method allows large-scale analysis of the composition of the top results from commercial search engines. It allows using valid empirical data to determine what users actually see on the search engine result pages.
- Date
- 20. 1.2015 18:30:22
-
Lewandowski, D.: Query understanding (2011)
0.01
0.006360204 = product of:
0.025440816 = sum of:
0.025440816 = product of:
0.05088163 = sum of:
0.05088163 = weight(_text_:22 in 344) [ClassicSimilarity], result of:
0.05088163 = score(doc=344,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.30952093 = fieldWeight in 344, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=344)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 18. 9.2018 18:22:18
-
Schaat, S.: Von der automatisierten Manipulation zur Manipulation der Automatisierung (2019)
0.01
0.006360204 = product of:
0.025440816 = sum of:
0.025440816 = product of:
0.05088163 = sum of:
0.05088163 = weight(_text_:22 in 4996) [ClassicSimilarity], result of:
0.05088163 = score(doc=4996,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.30952093 = fieldWeight in 4996, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=4996)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 19. 2.2019 17:22:00
-
Fluhr, C.: Crosslingual access to photo databases (2012)
0.00
0.0047701527 = product of:
0.019080611 = sum of:
0.019080611 = product of:
0.038161222 = sum of:
0.038161222 = weight(_text_:22 in 93) [ClassicSimilarity], result of:
0.038161222 = score(doc=93,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.23214069 = fieldWeight in 93, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=93)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 17. 4.2012 14:25:22
-
Lewandowski, D.: ¬Die Macht der Suchmaschinen und ihr Einfluss auf unsere Entscheidungen (2014)
0.00
0.0047701527 = product of:
0.019080611 = sum of:
0.019080611 = product of:
0.038161222 = sum of:
0.038161222 = weight(_text_:22 in 1491) [ClassicSimilarity], result of:
0.038161222 = score(doc=1491,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.23214069 = fieldWeight in 1491, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=1491)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 22. 9.2014 18:54:11
-
Huvila, I.: Affective capitalism of knowing and the society of search engine (2016)
0.00
0.0047701527 = product of:
0.019080611 = sum of:
0.019080611 = product of:
0.038161222 = sum of:
0.038161222 = weight(_text_:22 in 3246) [ClassicSimilarity], result of:
0.038161222 = score(doc=3246,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.23214069 = fieldWeight in 3246, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=3246)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 20. 1.2015 18:30:22
-
Chaudiron, S.; Ihadjadene, M.: Studying Web search engines from a user perspective : key concepts and main approaches (2012)
0.00
0.003975128 = product of:
0.015900511 = sum of:
0.015900511 = product of:
0.031801023 = sum of:
0.031801023 = weight(_text_:22 in 109) [ClassicSimilarity], result of:
0.031801023 = score(doc=109,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.19345059 = fieldWeight in 109, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=109)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 20. 4.2012 13:22:37
-
Lewandowski, D.; Spree, U.: Ranking of Wikipedia articles in search engines revisited : fair ranking for reasonable quality? (2011)
0.00
0.003975128 = product of:
0.015900511 = sum of:
0.015900511 = product of:
0.031801023 = sum of:
0.031801023 = weight(_text_:22 in 444) [ClassicSimilarity], result of:
0.031801023 = score(doc=444,freq=2.0), product of:
0.16438834 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04694356 = queryNorm
0.19345059 = fieldWeight in 444, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=444)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 30. 9.2012 19:27:22
