With university rankings gaining both in popularity and influence, university administrators develop strategies to improve their rankings. To better understand this competition for ranking, we present an adjacent category logit model to address the localized nature of ranking competition and include lagged rank as an independent variable to account for stickiness of ranking. Calibrating our mod...

Domain specific search focus on one area of knowledge. Applying broad based ranking algorithm to vertical search domains is not desirable. Broad based ranking model is built upon the data from multiple domains Vertical search engines use a focused crawler that attempts to index only relevant web pages to a pre defined topic. With Ranking Adaptation Model we can adapt an existing ranking model t...

Human gait has been shown to be an efficient biometric measure for person identification at a distance. However, it often needs different gait features to handle various covariate conditions including viewing angles, walking speed, carrying an object and wearing different types of shoes. In order to improve the robustness of gait-based person re-identification on such multi-covariate conditions...

Rankcluster is the first R package dedicated to ranking data. This package proposes modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting Rank (isr) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account m...

This paper is concerned with relevance ranking in search, particularly that using term dependency information. It proposes a novel and unified approach to relevance ranking using the kernel technique in statistical learning. In the approach, the general ranking model is defined as a kernel function of query and document representations. A number of kernel functions are proposed as specific rank...

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we p...

The research on supplier selection is abundant and the works usually only consider the critical success factors in the buyer–supplier relationship. However, the negative aspects of the buyer–supplier relationship must also be considered simultaneously. In this paper we propose a comprehensive model for ranking an arbitrary number of suppliers, selecting a number of them and allocating a quota o...

As we know, in evaluating of DMUs some of them might be efficient, so ranking of them have a high significant. One of the ranking methods is cross-efficiency. Cross efficiency evaluation in data envelopment analysis (DEA) is a commonly used skill for ranking decision making units (DMUs). Since, many studies ignore the intra-organizational communication and consider DMUs as a black box. For sign...

Analytic hierarchy process (AHP) is a frequently used method for ranking alternatives. The alternatives to be ranked are modeled based on a set of criteria. The ranking computed by AHP is trusted on faith notwithstanding the fact that a model is an approximation and liable to failure. We present a model to estimate the trust in the AHP ranking. The model helps in determining if it is prudent to...

Our research addresses the task of updating legal documents when new information emerges. In this paper, we employ a hierarchical ranking model to the task of updating legal documents. Word clustering features are incorporated to the ranking models to exploit semantic relations between words. Experimental results on legal data built from the United States Code show that the hierarchical ranking...