-
Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
0.03
0.032670997 = product of:
0.09801299 = sum of:
0.09801299 = sum of:
0.05284806 = weight(_text_:methodology in 2591) [ClassicSimilarity], result of:
0.05284806 = score(doc=2591,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.24885213 = fieldWeight in 2591, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.0390625 = fieldNorm(doc=2591)
0.04516493 = weight(_text_:22 in 2591) [ClassicSimilarity], result of:
0.04516493 = score(doc=2591,freq=4.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.27358043 = fieldWeight in 2591, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=2591)
0.33333334 = coord(1/3)
- Abstract
- Purpose In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues. Design/methodology/approach This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods. Findings The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments. Originality/value Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.
- Date
- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
-
Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995)
0.03
0.028261498 = product of:
0.08478449 = sum of:
0.08478449 = sum of:
0.05284806 = weight(_text_:methodology in 5697) [ClassicSimilarity], result of:
0.05284806 = score(doc=5697,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.24885213 = fieldWeight in 5697, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.0390625 = fieldNorm(doc=5697)
0.03193643 = weight(_text_:22 in 5697) [ClassicSimilarity], result of:
0.03193643 = score(doc=5697,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.19345059 = fieldWeight in 5697, 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=5697)
0.33333334 = coord(1/3)
- Abstract
- The performance of 8 ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. Focuses on the identification of algorithms that most effectively take cognizance of user preferences. user choices (i.e. the terms selected by the searchers for the query expansion search) provided the yardstick for the evaluation of the 8 ranking algorithms. This methodology introduces a user oriented approach in evaluating ranking algorithms for query expansion in contrast to the standard, system oriented approaches. Similarities in the performance of the 8 algorithms and the ways these algorithms rank terms were the main focus of this evaluation. The findings demonstrate that the r-lohi, wpq, enim, and porter algorithms have similar performance in bringing good terms to the top of a ranked list of terms for query expansion. However, further evaluation of the algorithms in different (e.g. full text) environments is needed before these results can be generalized beyond the context of the present study
- Date
- 22. 2.1996 13:14:10
-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
0.02
0.017032763 = product of:
0.051098287 = sum of:
0.051098287 = product of:
0.102196574 = sum of:
0.102196574 = weight(_text_:22 in 402) [ClassicSimilarity], result of:
0.102196574 = score(doc=402,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.61904186 = fieldWeight in 402, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.125 = fieldNorm(doc=402)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- 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)
0.01
0.014903667 = product of:
0.044711 = sum of:
0.044711 = product of:
0.089422 = sum of:
0.089422 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
0.089422 = score(doc=2134,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.5416616 = fieldWeight in 2134, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.109375 = fieldNorm(doc=2134)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 30. 3.2001 13:32:22
-
Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
0.01
0.014903667 = product of:
0.044711 = sum of:
0.044711 = product of:
0.089422 = sum of:
0.089422 = weight(_text_:22 in 3445) [ClassicSimilarity], result of:
0.089422 = score(doc=3445,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.5416616 = fieldWeight in 3445, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.109375 = fieldNorm(doc=3445)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 25. 8.2005 17:42:22
-
Stock, W.G.: On relevance distributions (2006)
0.01
0.014092816 = product of:
0.042278446 = sum of:
0.042278446 = product of:
0.08455689 = sum of:
0.08455689 = weight(_text_:methodology in 5116) [ClassicSimilarity], result of:
0.08455689 = score(doc=5116,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.3981634 = fieldWeight in 5116, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.0625 = fieldNorm(doc=5116)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- There are at least three possible ways that documents are distributed by relevance: informetric (power law), inverse logistic, and dichotomous. The nature of the type of distribution has implications for the construction of relevance ranking algorithms for search engines, for automated (blind) relevance feedback, for user behavior when using Web search engines, for combining of outputs of search engines for metasearch, for topic detection and tracking, and for the methodology of evaluation of information retrieval systems.
-
Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
0.01
0.012774572 = product of:
0.038323715 = sum of:
0.038323715 = product of:
0.07664743 = sum of:
0.07664743 = weight(_text_:22 in 58) [ClassicSimilarity], result of:
0.07664743 = score(doc=58,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.46428138 = fieldWeight in 58, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.09375 = fieldNorm(doc=58)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 14. 6.2015 22:12:44
-
Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986)
0.01
0.012774572 = product of:
0.038323715 = sum of:
0.038323715 = product of:
0.07664743 = sum of:
0.07664743 = weight(_text_:22 in 2051) [ClassicSimilarity], result of:
0.07664743 = score(doc=2051,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.46428138 = fieldWeight in 2051, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.09375 = fieldNorm(doc=2051)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 14. 6.2015 22:12:56
-
Sánchez-de-Madariaga, R.; Fernández-del-Castillo, J.R.: ¬The bootstrapping of the Yarowsky algorithm in real corpora (2009)
0.01
0.012331214 = product of:
0.03699364 = sum of:
0.03699364 = product of:
0.07398728 = sum of:
0.07398728 = weight(_text_:methodology in 2451) [ClassicSimilarity], result of:
0.07398728 = score(doc=2451,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.348393 = fieldWeight in 2451, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.0546875 = fieldNorm(doc=2451)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- The Yarowsky bootstrapping algorithm resolves the homograph-level word sense disambiguation (WSD) problem, which is the sense granularity level required for real natural language processing (NLP) applications. At the same time it resolves the knowledge acquisition bottleneck problem affecting most WSD algorithms and can be easily applied to foreign language corpora. However, this paper shows that the Yarowsky algorithm is significantly less accurate when applied to domain fluctuating, real corpora. This paper also introduces a new bootstrapping methodology that performs much better when applied to these corpora. The accuracy achieved in non-domain fluctuating corpora is not reached due to inherent domain fluctuation ambiguities.
-
Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000)
0.01
0.010569612 = product of:
0.031708833 = sum of:
0.031708833 = product of:
0.063417666 = sum of:
0.063417666 = weight(_text_:methodology in 105) [ClassicSimilarity], result of:
0.063417666 = score(doc=105,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.29862255 = fieldWeight in 105, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.046875 = fieldNorm(doc=105)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
-
Lin, J.; Katz, B.: Building a reusable test collection for question answering (2006)
0.01
0.010569612 = product of:
0.031708833 = sum of:
0.031708833 = product of:
0.063417666 = sum of:
0.063417666 = weight(_text_:methodology in 5045) [ClassicSimilarity], result of:
0.063417666 = score(doc=5045,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.29862255 = fieldWeight in 5045, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.046875 = fieldNorm(doc=5045)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- In contrast to traditional information retrieval systems, which return ranked lists of documents that users must manually browse through, a question answering system attempts to directly answer natural language questions posed by the user. Although such systems possess language-processing capabilities, they still rely on traditional document retrieval techniques to generate an initial candidate set of documents. In this article, the authors argue that document retrieval for question answering represents a task different from retrieving documents in response to more general retrospective information needs. Thus, to guide future system development, specialized question answering test collections must be constructed. They show that the current evaluation resources have major shortcomings; to remedy the situation, they have manually created a small, reusable question answering test collection for research purposes. In this article they describe their methodology for building this test collection and discuss issues they encountered regarding the notion of "answer correctness."
-
MacFarlane, A.; McCann, J.A.; Robertson, S.E.: Parallel methods for the generation of partitioned inverted files (2005)
0.01
0.010569612 = product of:
0.031708833 = sum of:
0.031708833 = product of:
0.063417666 = sum of:
0.063417666 = weight(_text_:methodology in 651) [ClassicSimilarity], result of:
0.063417666 = score(doc=651,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.29862255 = fieldWeight in 651, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.046875 = fieldNorm(doc=651)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- Purpose - The generation of inverted indexes is one of the most computationally intensive activities for information retrieval systems: indexing large multi-gigabyte text databases can take many hours or even days to complete. We examine the generation of partitioned inverted files in order to speed up the process of indexing. Two types of index partitions are investigated: TermId and DocId. Design/methodology/approach - We use standard measures used in parallel computing such as speedup and efficiency to examine the computing results and also the space costs of our trial indexing experiments. Findings - The results from runs on both partitioning methods are compared and contrasted, concluding that DocId is the more efficient method. Practical implications - The practical implications are that the DocId partitioning method would in most circumstances be used for distributing inverted file data in a parallel computer, particularly if indexing speed is the primary consideration. Originality/value - The paper is of value to database administrators who manage large-scale text collections, and who need to use parallel computing to implement their text retrieval services.
-
Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013)
0.01
0.010569612 = product of:
0.031708833 = sum of:
0.031708833 = product of:
0.063417666 = sum of:
0.063417666 = weight(_text_:methodology in 1035) [ClassicSimilarity], result of:
0.063417666 = score(doc=1035,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.29862255 = fieldWeight in 1035, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.046875 = fieldNorm(doc=1035)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- User interest as a very dynamic information need is often ignored in most existing information retrieval systems. In this research, we present the results of experiments designed to evaluate the performance of a real-time interest model (RIM) that attempts to identify the dynamic and changing query level interests regarding social media outputs. Unlike most existing ranking methods, our ranking approach targets calculation of the probability that user interest in the content of the document is subject to very dynamic user interest change. We describe 2 formulations of the model (real-time interest vector space and real-time interest language model) stemming from classical relevance ranking methods and develop a novel methodology for evaluating the performance of RIM using Amazon Mechanical Turk to collect (interest-based) relevance judgments on a daily basis. Our results show that the model usually, although not always, performs better than baseline results obtained from commercial web search engines. We identify factors that affect RIM performance and outline plans for future research.
-
Li, J.; Willett, P.: ArticleRank : a PageRank-based alternative to numbers of citations for analysing citation networks (2009)
0.01
0.00880801 = product of:
0.02642403 = sum of:
0.02642403 = product of:
0.05284806 = sum of:
0.05284806 = weight(_text_:methodology in 751) [ClassicSimilarity], result of:
0.05284806 = score(doc=751,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.24885213 = fieldWeight in 751, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.0390625 = fieldNorm(doc=751)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- Purpose - The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach - ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets - a citation network based on an early paper on webometrics, and a self-citation network based on the 19 most cited papers in the Journal of Documentation - using citation data taken from the Web of Knowledge database. Findings - ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value - This is a novel application of the PageRank algorithm.
-
MacFarlane, A.; McCann, J.A.; Robertson, S.E.: Parallel methods for the update of partitioned inverted files (2007)
0.01
0.00880801 = product of:
0.02642403 = sum of:
0.02642403 = product of:
0.05284806 = sum of:
0.05284806 = weight(_text_:methodology in 819) [ClassicSimilarity], result of:
0.05284806 = score(doc=819,freq=2.0), product of:
0.21236731 = queryWeight, product of:
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.047143444 = queryNorm
0.24885213 = fieldWeight in 819, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.504705 = idf(docFreq=1328, maxDocs=44218)
0.0390625 = fieldNorm(doc=819)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- Purpose - An issue that tends to be ignored in information retrieval is the issue of updating inverted files. This is largely because inverted files were devised to provide fast query service, and much work has been done with the emphasis strongly on queries. This paper aims to study the effect of using parallel methods for the update of inverted files in order to reduce costs, by looking at two types of partitioning for inverted files: document identifier and term identifier. Design/methodology/approach - Raw update service and update with query service are studied with these partitioning schemes using an incremental update strategy. The paper uses standard measures used in parallel computing such as speedup to examine the computing results and also the costs of reorganising indexes while servicing transactions. Findings - Empirical results show that for both transaction processing and index reorganisation the document identifier method is superior. However, there is evidence that the term identifier partitioning method could be useful in a concurrent transaction processing context. Practical implications - There is an increasing need to service updates, which is now becoming a requirement of inverted files (for dynamic collections such as the web), demonstrating that a shift in requirements of inverted file maintenance is needed from the past. Originality/value - The paper is of value to database administrators who manage large-scale and dynamic text collections, and who need to use parallel computing to implement their text retrieval services.
-
MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004)
0.01
0.0085163815 = product of:
0.025549144 = sum of:
0.025549144 = product of:
0.051098287 = sum of:
0.051098287 = weight(_text_:22 in 5108) [ClassicSimilarity], result of:
0.051098287 = score(doc=5108,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.30952093 = fieldWeight in 5108, 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=5108)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 20. 1.2007 18:30:22
-
Faloutsos, C.: Signature files (1992)
0.01
0.0085163815 = product of:
0.025549144 = sum of:
0.025549144 = product of:
0.051098287 = sum of:
0.051098287 = weight(_text_:22 in 3499) [ClassicSimilarity], result of:
0.051098287 = score(doc=3499,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.30952093 = fieldWeight in 3499, 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=3499)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 7. 5.1999 15:22:48
-
Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003)
0.01
0.0085163815 = product of:
0.025549144 = sum of:
0.025549144 = product of:
0.051098287 = sum of:
0.051098287 = weight(_text_:22 in 1422) [ClassicSimilarity], result of:
0.051098287 = score(doc=1422,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.30952093 = fieldWeight in 1422, 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=1422)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 22. 3.2003 19:27:23
-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
0.01
0.0085163815 = product of:
0.025549144 = sum of:
0.025549144 = product of:
0.051098287 = sum of:
0.051098287 = weight(_text_:22 in 1431) [ClassicSimilarity], result of:
0.051098287 = score(doc=1431,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.30952093 = fieldWeight in 1431, 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=1431)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 22. 8.2014 17:05:18
-
Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014)
0.01
0.0085163815 = product of:
0.025549144 = sum of:
0.025549144 = product of:
0.051098287 = sum of:
0.051098287 = weight(_text_:22 in 1484) [ClassicSimilarity], result of:
0.051098287 = score(doc=1484,freq=2.0), product of:
0.16508831 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047143444 = queryNorm
0.30952093 = fieldWeight in 1484, 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=1484)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 13. 9.2014 14:45:22
