A Study on Ranking Method in Retrieving Web Pages Based on Content and Link Analysis: Combination of Fourier Domain Scoring and Pagerank Scoring

Ranking module is an important component of search process which sorts through relevant pages. Since collection of Web pages has additional information inherent in the hyperlink structure of the Web, it can be represented as link score and then combined with the usual information retrieval techniques of content score. In this paper we report our studies about ranking score of Web pages combined from link analysis, PageRank Scoring, and content analysis, Fourier Domain Scoring. Our experiments use collection of Web pages relate to Statistic subject from Wikipedia with objectives to check correctness and performance evaluation of combination ranking method. Evaluation of PageRank Scoring show that the highest score does not always relate to Statistic. Since the links within Wikipedia articles exists so that users are always one click away from more information on any point that has a link attached, it it possible that unrelated topics to Statistic are most likely frequently mentioned in the collection. While the combination method show link score which is given proportional weight to content score of Web pages does effect the retrieval results

On Ranking and Selection from Independent Truncated Normal Distributions

This paper develops probability statements and ranking and selection rules for independent truncated normal populations. An application to a broad class of parametric stochastic frontier models is considered, where interest centers on making probability statements concerning unobserved firm-level technical ineffciency. In particular, probabilistic decision rules allow subsets of firms to be deemed relatively effcient or ineffcient at pre-specified probabilities. An empirical example is provided.Ranking and Selection, Truncated Normal, Stochastic Frontier

On selection procedures based on ranks - Counterexamples concerning least favorable configurations

Multiple decision procedures based on ranking methods proprosed for analyzing data in one-way layou

Influence of aggregation and measurement scale on ranking a compromise alternative in AHP

Analytic Hierarchy Process (AHP) is one of the most popular multi-attribute decision aid methods. However, within AHP, there are several competing preference measurement scales and aggregation techniques. In this paper, we compare these possibilities using a decision problem with an inherent trade-off between two criteria. A decision-maker has to choose among three alternatives: two extremes and one compromise. Six different measurement scales described previously in the literature and the new proposed logarithmic scale are considered for applying the additive and the multiplicative aggregation techniques. The results are compared with the standard consumer choice theory. We find that with the geometric and power scales a compromise is never selected when aggregation is additive and rarely when aggregation is multiplicative, while the logarithmic scale used with the multiplicative aggregation most often selects the compromise that is desirable by consumer choice theory.AHP, Multi-criteria Decision analysis

DISTANCE MEASURES IN AGGREGATING PREFERENCE DATA

The aim of this paper is to present aggregation methods of individual preferences scores by means of distance measures. Three groups of distance measures are discussed: measures  which use preference distributions for all pairs of objects (e.g. Kemeny’s measure, Bogart’s measure), distance measures based on ranking data (e.g. Spearman distance, Podani distance) and distance measures using permissible transformations to ordinal scale (GDM2 distance). Adequate distance formulas are presented and the aggregation of individual preference by using separate distance measures was carried out with the use of the R program

Investigations on ranking of progeny tested bulls

International audienc

Survey on Ranking Fraud for Mobile Apps

In today's world there are many fraud ways through which app developers try to put their app at the first position. The developers try hard to configure the positions of various apps in the list of apps in that particular area. Mobile phones operating system is developing day by day but research in fraud apps is limited or not much discovered. Fraud ranking in mobile phones lead to download of the false app which allows damaging the mobile phones and falsely getting famous by that false apps. Fraud ranking in mobile phones is very important and this paper shows the misinterpretation of the apps information and configured apps position. Also a framework is used for fraud detection in apps. The work is grouped basically into three categories. First is web ranking spam detection, second is the online review spam detection and third one is mobile app recommendation. The first method Web ranking spam refers to any kind of actions which bring to selected Web pages an unjustifiable favorable relevance or give much importance. The second one is Review spam which is designed to give unfair view of some objects so as to influence the consumers' perception of the objects by directly or indirectly damaging the object's reputation. The third one is mobile app recommendation which tells users to check the app usage record