-
Bauckhage, C.: Marginalizing over the PageRank damping factor (2014)
0.01
0.014605265 = product of:
0.02921053 = sum of:
0.02921053 = product of:
0.05842106 = sum of:
0.05842106 = weight(_text_:classification in 928) [ClassicSimilarity], result of:
0.05842106 = score(doc=928,freq=2.0), product of:
0.16603322 = queryWeight, product of:
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.05213454 = queryNorm
0.35186368 = fieldWeight in 928, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.078125 = fieldNorm(doc=928)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- In this note, we show how to marginalize over the damping parameter of the PageRank equation so as to obtain a parameter-free version known as TotalRank. Our discussion is meant as a reference and intended to provide a guided tour towards an interesting result that has applications in information retrieval and classification.
-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
0.01
0.014127021 = product of:
0.028254041 = sum of:
0.028254041 = product of:
0.056508083 = sum of:
0.056508083 = weight(_text_:22 in 1431) [ClassicSimilarity], result of:
0.056508083 = score(doc=1431,freq=2.0), product of:
0.18256627 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05213454 = 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)
0.5 = coord(1/2)
- 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
0.014127021 = product of:
0.028254041 = sum of:
0.028254041 = product of:
0.056508083 = sum of:
0.056508083 = weight(_text_:22 in 1484) [ClassicSimilarity], result of:
0.056508083 = score(doc=1484,freq=2.0), product of:
0.18256627 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05213454 = 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)
0.5 = coord(1/2)
- Date
- 13. 9.2014 14:45:22
-
González-Ibáñez, R.; Esparza-Villamán, A.; Vargas-Godoy, J.C.; Shah, C.: ¬A comparison of unimodal and multimodal models for implicit detection of relevance in interactive IR (2019)
0.01
0.012648531 = product of:
0.025297062 = sum of:
0.025297062 = product of:
0.050594125 = sum of:
0.050594125 = weight(_text_:classification in 5417) [ClassicSimilarity], result of:
0.050594125 = score(doc=5417,freq=6.0), product of:
0.16603322 = queryWeight, product of:
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.05213454 = queryNorm
0.3047229 = fieldWeight in 5417, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.0390625 = fieldNorm(doc=5417)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Implicit detection of relevance has been approached by many during the last decade. From the use of individual measures to the use of multiple features from different sources (multimodality), studies have shown the feasibility to automatically detect whether a document is relevant. Despite promising results, it is not clear yet to what extent multimodality constitutes an effective approach compared to unimodality. In this article, we hypothesize that it is possible to build unimodal models capable of outperforming multimodal models in the detection of perceived relevance. To test this hypothesis, we conducted three experiments to compare unimodal and multimodal classification models built using a combination of 24 features. Our classification experiments showed that a univariate unimodal model based on the left-click feature supports our hypothesis. On the other hand, our prediction experiment suggests that multimodality slightly improves early classification compared to the best unimodal models. Based on our results, we argue that the feasibility for practical applications of state-of-the-art multimodal approaches may be strongly constrained by technology, cultural, ethical, and legal aspects, in which case unimodality may offer a better alternative today for supporting relevance detection in interactive information retrieval systems.
-
Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
0.01
0.012486639 = product of:
0.024973279 = sum of:
0.024973279 = product of:
0.049946558 = sum of:
0.049946558 = weight(_text_:22 in 2591) [ClassicSimilarity], result of:
0.049946558 = score(doc=2591,freq=4.0), product of:
0.18256627 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05213454 = 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)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
-
Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012)
0.01
0.012392979 = product of:
0.024785958 = sum of:
0.024785958 = product of:
0.049571916 = sum of:
0.049571916 = weight(_text_:classification in 92) [ClassicSimilarity], result of:
0.049571916 = score(doc=92,freq=4.0), product of:
0.16603322 = queryWeight, product of:
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.05213454 = queryNorm
0.29856625 = fieldWeight in 92, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.046875 = fieldNorm(doc=92)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.
-
Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012)
0.01
0.008829388 = product of:
0.017658776 = sum of:
0.017658776 = product of:
0.03531755 = sum of:
0.03531755 = weight(_text_:22 in 241) [ClassicSimilarity], result of:
0.03531755 = score(doc=241,freq=2.0), product of:
0.18256627 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05213454 = queryNorm
0.19345059 = fieldWeight in 241, 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=241)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 11. 6.2012 14:22:34
-
Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013)
0.01
0.008829388 = product of:
0.017658776 = sum of:
0.017658776 = product of:
0.03531755 = sum of:
0.03531755 = weight(_text_:22 in 664) [ClassicSimilarity], result of:
0.03531755 = score(doc=664,freq=2.0), product of:
0.18256627 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05213454 = queryNorm
0.19345059 = fieldWeight in 664, 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=664)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 22. 3.2013 19:34:49
-
Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011)
0.01
0.00876316 = product of:
0.01752632 = sum of:
0.01752632 = product of:
0.03505264 = sum of:
0.03505264 = weight(_text_:classification in 1165) [ClassicSimilarity], result of:
0.03505264 = score(doc=1165,freq=2.0), product of:
0.16603322 = queryWeight, product of:
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.05213454 = queryNorm
0.21111822 = fieldWeight in 1165, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.046875 = fieldNorm(doc=1165)
0.5 = coord(1/2)
0.5 = coord(1/2)
-
Koumenides, C.L.; Shadbolt, N.R.: Ranking methods for entity-oriented semantic web search (2014)
0.01
0.00876316 = product of:
0.01752632 = sum of:
0.01752632 = product of:
0.03505264 = sum of:
0.03505264 = weight(_text_:classification in 1280) [ClassicSimilarity], result of:
0.03505264 = score(doc=1280,freq=2.0), product of:
0.16603322 = queryWeight, product of:
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.05213454 = queryNorm
0.21111822 = fieldWeight in 1280, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.046875 = fieldNorm(doc=1280)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- This article provides a technical review of semantic search methods used to support text-based search over formal Semantic Web knowledge bases. Our focus is on ranking methods and auxiliary processes explored by existing semantic search systems, outlined within broad areas of classification. We present reflective examples from the literature in some detail, which should appeal to readers interested in a deeper perspective on the various methods and systems implemented in the outlined literature. The presentation covers graph exploration and propagation methods, adaptations of classic probabilistic retrieval models, and query-independent link analysis via flexible extensions to the PageRank algorithm. Future research directions are discussed, including development of more cohesive retrieval models to unlock further potentials and uses, data indexing schemes, integration with user interfaces, and building community consensus for more systematic evaluation and gradual development.
-
Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014)
0.01
0.00876316 = product of:
0.01752632 = sum of:
0.01752632 = product of:
0.03505264 = sum of:
0.03505264 = weight(_text_:classification in 2799) [ClassicSimilarity], result of:
0.03505264 = score(doc=2799,freq=2.0), product of:
0.16603322 = queryWeight, product of:
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.05213454 = queryNorm
0.21111822 = fieldWeight in 2799, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.046875 = fieldNorm(doc=2799)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.
-
Jacucci, G.; Barral, O.; Daee, P.; Wenzel, M.; Serim, B.; Ruotsalo, T.; Pluchino, P.; Freeman, J.; Gamberini, L.; Kaski, S.; Blankertz, B.: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval (2019)
0.01
0.0073026326 = product of:
0.014605265 = sum of:
0.014605265 = product of:
0.02921053 = sum of:
0.02921053 = weight(_text_:classification in 5356) [ClassicSimilarity], result of:
0.02921053 = score(doc=5356,freq=2.0), product of:
0.16603322 = queryWeight, product of:
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.05213454 = queryNorm
0.17593184 = fieldWeight in 5356, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.1847067 = idf(docFreq=4974, maxDocs=44218)
0.0390625 = fieldNorm(doc=5356)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).
