Preference relations based unsupervised rank aggregation for metasearch

Desarkar, Maunendra Sankar; Sarkar, Sudeshna; Mitra, Pabitra

doi:10.1016/j.eswa.2015.12.005

Cited by 38 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Early efforts on rank aggregation can be traced back to studies on social choice theory and political elections (de Borda, 1781). Since then, with the rise of internet and web search engines, rank aggregation has drawn much attention and is widely applied to meta-search (Desarkar, Sarkar, & Mitra, 2016;Manmatha, Rath, & Feng, 2001;Manmatha & Sever, 2002), document analysis (Hull, Pedersen, & Schütze, 1996), and similarity search in databases (Fagin, Lotem, & Naor, 2003). Among all rank aggregation methods, Borda-inspired methods are easy to understand and widely used (Lin, 2010).…”

Section: Rank Aggregation Methodsmentioning

confidence: 99%

“…5. Unlike the supervised rank aggregation methods, the weighted rank aggregation method proposed by Desarkar et al (2016) is an unsupervised method for rank aggregation by assigning different weights to different rankers according to their ranking qualities measured in terms of their own agreements with "majority" of rankers. 6.…”

Section: Rank Aggregation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of ranking data

2019

WIREs Computational Stats

View full text Add to dashboard Cite

Ranking is one of the simple and efficient data collection techniques to understand individuals' perception and preferences for some items such as products, people, and species. Ranking data are frequently collected when individuals are asked to rank a set of items according to a certain preference criterion. Over the years, many statistical models and methods have been developed for analyzing ranking data. This paper will give a literature review of these models and methods and present the recent advances of the analysis of ranking data. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Nonparametric Methods

show abstract

Section: Rank Aggregation Methodsmentioning

confidence: 99%

Section: Rank Aggregation Methodsmentioning

confidence: 99%

Analysis of ranking data

2019

WIREs Computational Stats

View full text Add to dashboard Cite

show abstract

“…Depending on the reference framework, this problem is known as a social choice problem, a rank aggregation problem, a median ranking problem, a central ranking detection, or a Kemeny problem (Kemeny and Snell, 1962;Marden, 1996;Fiirnkranz and Hiillermeier, 2010;Amodio et al, 2016). This (NP-hard) problem has received increasing importance over the years, both as a main research task and as an essential starting point for other types of analysis (Corain and Salmaso, 2007;Fields et al, 2013;Heiser and D'Ambrosia, 2013;Desarkar et al, 2016;D'Ambrosia and Heiser, 2016;Telcs et al, 2016;Sciandra et al, 2017;Dopazo and Martfnez Céspedes, 2017;Svendova and Schimek, 2017). The rank aggregation problem has been approached using several different approaches, some of which work only when complete rankings are used as the input and produce an output that is a full con sensus ranking (Meila et al, 2007;Aledo et al, 2013;D'Ambrosia et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Median constrained bucket order rank aggregation

et al. 2019

View full text Add to dashboard Cite

The rank aggregation problem can be summarized as the problem of aggregating individua! preferences expressed by a set of judges to obtain a ranking that represents the best synthesis of their choices. Several approaches for handling this problem have been proposed and are generally linked with either axiomatic frameworks or alternative strategies. In this paper, we present a new definition of median ranking and frame it within the Kemeny's axiomatic framework. Moreover, we show the usefulness of our approach in a practical case about triage prioritization.

show abstract

“…In the web page retrieval process, the information collected from the individual rankers is plentiful. Rank aggregation [6] is a commonly used technique in metasearch engine applications [7][8][9], due to the frame work of WWW. Re-ranking [10][11][12][13][14] is performed in the meta-search engine [15,16] by combining the results of underlying search engines.…”

Section: Introductionmentioning

confidence: 99%

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

Vijaya

Chander

2018

Sci. China Inf. Sci.

View full text Add to dashboard Cite

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.

show abstract

Preference relations based unsupervised rank aggregation for metasearch

Cited by 38 publications

References 21 publications

Analysis of ranking data

Analysis of ranking data

Median constrained bucket order rank aggregation

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

Contact Info

Product

Resources

About