Effective rank aggregation for metasearching

Akritidis, Leonidas; Katsaros, Dimitrios; Bozanis, Panayiotis

doi:10.1016/j.jss.2010.09.001

Cited by 37 publications

(14 citation statements)

References 20 publications

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“…This section presents the experimental results of the proposed LionRank search engine and the performance evaluation of the proposed method is compared with various existing search engines such as QuadRank [21], Outrank [22], Google, Bing and Yahoo.…”

Section: Resultsmentioning

confidence: 99%

LionRank: lion algorithm-based metasearch engines for re-ranking of webpages

Vijaya

Chander

2018

Sci. China Inf. Sci.

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Due to the rapid growth of the web, the process of collecting the relevant web pages based on the user query is one of the major challenging tasks in recent days. Hence, it is very complicated for the users to know the most relevant information even though various search engines are widely employed. To deal with users' trouble in identifying the relevant information from the web, we have proposed a meta-lion search engine to capture and analyze the ranking scores of various search engines and thereby, generate the re-ranked score results. Accordingly, LionRank, a lion algorithm-based meta-search engine is proposed for the re-ranking of the web pages. Here, different features like text based, factor based, rank based and classifier based features are used by the underlying search engines. In classifier based feature extraction, we have used the fuzzy integrated extended nearest neighbor (FENN) classifier to include the semantics in feature extraction. Moreover, an intelligent re-ranking process is proposed based on the lion algorithm to fuse the features scores optimally. Finally, the results of the proposed LionRank is analyzed with the web page database collected through four benchmark queries, and the quantitative performance are analyzed using precision, recall, and F-score. From the results, we proved that the proposed LionRank obtained the maximum F-score of 81% as compared with that of existing search engines like QuadRank, Outrank, Google, Yahoo, and Bing.

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Section: Resultsmentioning

confidence: 99%

LionRank: lion algorithm-based metasearch engines for re-ranking of webpages

Vijaya

Chander

2018

Sci. China Inf. Sci.

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“…In the case of meta-search engines, some authors suggested ways to improve the precision of the combination of results based on Borda's Count. Akritidis, Katsaros, and Bozanis (2011) presented the QuadRank aggregation method, which takes into consideration additional parameters, and Husain, Prakash and Khan (2012) suggested joining partial lists obtained from various search engines with a weighted bipartite graph.…”

Section: Conceptual and Methodological Backgroundmentioning

confidence: 99%

A Webometric Analysis of Travel Blogs and Review Hosting: The Case of Catalonia

Mariné-Roig¹

2014

Journal of Travel & Tourism Marketing

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Today, destination-marketing organizations and researchers are increasingly focusing their attention on travel blogs and reviews due to their potential for projecting the image of a specific destination and for influencing travel behavior and decision making. However, the criteria used to select the Web sites hosting travel blogs and reviews for study are unclear, and very few quantitative or demographic studies about users (bloggers and readers) have been conducted. The aim of this study is to propose a method in which a webometric analysis is used to select the most suitable Web sites for a specific case study and to obtain information about users. The proposed webometric analysis consists of an integrated formula including visibility, popularity, and size metrics. This method was used to rank 11 suitable Web sites for studying the case of Catalonia.

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“…Many other studies dealing with rank aggregation also have been proposed (Dwork, Kumar, Naor, & Sivakumar, ; Wei, Li, & Liu, ). The rank aggregation task that is encountered in many situations such as metasearch (Akritidis, Katsaros, & Bozanis, ; Aslam & Montague, ) consists of computing a consensus ranking given the individual ranking preferences of several judges (Renda & Straccia, ). Given the ranked lists of documents returned by multiple search engines in response to a given query, the problem of metasearch is to combine these lists in a way that optimizes the performance of the combination (Aslam & Montague, ).…”

Section: Multidimensional Relevance Aggregation In Irmentioning

confidence: 99%

iAggregator: Multidimensional relevance aggregation based on a fuzzy operator

Moulahi

Tamine

Yahia

2014

Asso for Info Science & Tech

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Recently, an increasing number of information retrieval studies have triggered a resurgence of interest in redefining the algorithmic estimation of relevance, which implies a shift from topical to multidimensional relevance assessment. A key underlying aspect that emerged when addressing this concept is the aggregation of the relevance assessments related to each of the considered dimensions. The most commonly adopted forms of aggregation are based on classical weighted means and linear combination schemes to address this issue. Although some initiatives were recently proposed, none was concerned with considering the inherent dependencies and interactions existing among the relevance criteria, as is the case in many real-life applications. In this article, we present a new fuzzy-based operator, called iAggregator, for multidimensional relevance aggregation. Its main originality, beyond its ability to model interactions between different relevance criteria, lies in its generalization of many classical aggregation functions. To validate our proposal, we apply our operator within a tweet search task. Experiments using a standard benchmark, namely, Text REtrieval Conference Microblog, 1 emphasize the relevance of our contribution when compared with traditional aggregation schemes. In addition, it outperforms state-of-the-art aggregation operators such as the Scoring and the And prioritized operators as well as some representative learning-to-rank algorithms. IntroductionMulticriteria aggregation is an issue that has been thoroughly addressed in social choice (Arrow, 1974;Condorcet, 1785;Fishburn, 1972), engineering design (Keeney & Raiffa, 1993;Neumann & Morgenstern, 1953), and computer vision applications (Dubois & Prade, 2004;Torra, 2005), to cite but a few. The multicriteria aggregation arises when for a given task there are several alternatives that have to be ordered with respect to different criteria and we are faced with the problem of combining them to figure out a ranking over the set of alternatives. The need to aggregate several inputs into a single representative output allowed successful applications of aggregation functions to fields as diverse as information retrieval (IR) (Farah & Vanderpooten, 2007) multiple criteria decision analysis (Grabisch, Kojadinovic, & Meyer, 2008;Steuer, 1986), data fusion (Ah-Pine, 2008;Vogt & Cottrell, 1999), and database retrieval (Le Calvè & Savoy, 2000). In this article, we are more interested in the IR field. Because ranking and relevance are at the heart of IR systems (Hawking, Craswell, Bailey, & Griffiths, 2001), a great deal of research has triggered a resurgence of interest in revisiting the concept of relevance considering several criteria. In fact, many of the proposed state-of-the-art early IR models rank documents by computing single scores separately with respect to one single objective criterion, rather than considering other relevance dimensions encompassing contextual features with respect to users or documents (Borlund, 2003). This most commonly used crit...

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Effective rank aggregation for metasearching

Cited by 37 publications

References 20 publications

LionRank: lion algorithm-based metasearch engines for re-ranking of webpages

LionRank: lion algorithm-based metasearch engines for re-ranking of webpages

A Webometric Analysis of Travel Blogs and Review Hosting: The Case of Catalonia

iAggregator: Multidimensional relevance aggregation based on a fuzzy operator

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