scispace - formally typeset
Search or ask a question
Topic

Pairwise comparison

About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, a hybrid multiple criteria decision making (MCDM) model is applied to select optimal supplier, which can lead to better criteria selection, is used to modify criteria.
Abstract: The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM) model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP) is then used to obtain their weights. To avoid calculation and additional pairwise comparisons of ANP, a technique for order preference by similarity to ideal solution (TOPSIS) is used to rank the alternatives. The use of a combination of the fuzzy Delphi method, ANP, and TOPSIS, proposing an MCDM model for supplier selection, and applying these to a real case are the unique features of this study.

53 citations

Journal ArticleDOI
TL;DR: The algorithm is based on a logarithmic transformation of the generalized pairwise comparisons matrix into a linear space with the Euclidean metric and is thus a generalization of the ordinary geometric means method.
Abstract: This paper presents an algorithm for computing a consistent approximation to a generalized pairwise comparisons matrix (that is, without the reciprocity property or even 1s on the main diagonal). The algorithm is based on a logarithmic transformation of the generalized pairwise comparisons matrix into a linear space with the Euclidean metric. It uses both the row and (reciprocals of) column geometric means and is thus a generalization of the ordinary geometric means method. The resulting approximation is not only consistent, but also closest to the original matrix, i.e., deviates least from an expert's original judgments. The computational complexity of the algorithm is O ( n 2 ).

53 citations

Proceedings ArticleDOI
09 Sep 2012
TL;DR: A MapReduce algorithm for the pairwise item comparison and top-N recommendation problem that scales linearly with respect to a growing number of users is developed.
Abstract: Similarity-based neighborhood methods, a simple and popular approach to collaborative filtering, infer their predictions by finding users with similar taste or items that have been similarly rated. If the number of users grows to millions, the standard approach of sequentially examining each item and looking at all interacting users does not scale. To solve this problem, we develop a MapReduce algorithm for the pairwise item comparison and top-N recommendation problem that scales linearly with respect to a growing number of users. This parallel algorithm is able to work on partitioned data and is general in that it supports a wide range of similarity measures. We evaluate our algorithm on a large dataset consisting of 700 million song ratings from Yahoo! Music.

53 citations

Journal ArticleDOI
TL;DR: In this article, a pseudo-likelihood estimation method for the grouped continuous model and its extension to mixed ordinal and continuous data is proposed as an alternative to maximum likelihood estimation, which advocates simply pooling marginal pairwise likelihoods to approximate the full likelihood.

52 citations

Journal ArticleDOI
TL;DR: An inferential strategy based on the pairwise likelihood, which only requires the computation of bivariate distributions, which has the potential to handle large data sets and improve on standard inferential procedures by means of bootstrap methods.
Abstract: Inference in generalized linear models with crossed effects is often made cumbersome by the high-dimensional intractable integrals involved in the likelihood function. We propose an inferential strategy based on the pairwise likelihood, which only requires the computation of bivariate distributions. The benefits of our approach are the simplicity of implementation and the potential to handle large data sets. The estimators based on the pairwise likelihood are generally consistent and asymptotically normally distributed. The pairwise likelihood makes it possible to improve on standard inferential procedures by means of bootstrap methods. The performance of the proposed methodology is illustrated by simulations and application to the well-known salamander mating data set.

52 citations


Network Information
Related Topics (5)
Markov chain
51.9K papers, 1.3M citations
81% related
Cluster analysis
146.5K papers, 2.9M citations
76% related
Deep learning
79.8K papers, 2.1M citations
75% related
Optimization problem
96.4K papers, 2.1M citations
74% related
Robustness (computer science)
94.7K papers, 1.6M citations
74% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20231,305
20222,607
2021581
2020554
2019520