Fuzzy-Weighted Ranked Set Sampling Method

Sampling method plays an important role for data collection in a scientific research. Ranked set sampling (RSS), which was first introduced by McIntyre, is an advanced method to obtain data for getting information and inference about the population of interest. The main impact of RSS is to use the ranking information of the units in the sampling mechanism. Even though most of theoretical inferences are made based on exact measurement of the variable of interest, the ranking process is done with an expert judgment or concomitant variable (without exact measurement) in practice. Because of the ambiguity in discriminating the rank of one unit with another, ranking the units could not be perfect, and it may cause uncertainty. There are some studies focused on the modeling of this uncertainty with a probabilistic perspective in the literature. In this chapter, another perspective, a fuzzy-set-inspired approach, for the uncertainty in the ranking mechanism of RSS is introduced.

Comprehensive Review and Future Research Directions on Dynamic Faceted Search

In modern society, the increasing number of web search operations on various search engines has become ubiquitous due to the significant number of results presented to the users and the incompetent result-ranking mechanism in some domains, such as medical, law, and academia. As a result, the user is overwhelmed with a large number of misranked or uncategorized search results. One of the most promising technologies to reduce the number of results and provide desirable information to the users is dynamic faceted filters. Therefore, this paper extensively reviews related research articles published in IEEE Xplore, Web of Science, and the ACM digital library. As a result, a total of 170 related research papers were considered and organized into five categories. The main contribution of this paper is to provide a detailed analysis of the faceted search’s fundamental attributes, as well as to demonstrate the motivation from the usage, concerns, challenges, and recommendations to enhance the use of the faceted approach among web search service providers.

Collaborative filtering based service ranking with invocation histories

Collaborative filtering based recommender systems have been very successful on dealing with the information overload problem and providing personalized recommendations to users. When more and more web services are published online, this technique can also help recommend and select services which satisfy users’ particular Quality of Service (QoS) requirements and preferences. In this thesis, we propose a novel collaborative filtering based service ranking mechanism, in which the invocation and query histories are used to infer the users’ preferences, and user similarity is calculated based on invocations and queries. To overcome some of the inherent problems with the collaborative filtering systems such as the cold start and data sparsity problem, the final ranking score is a combination of the QoS-based matching score and the collaborative filtering based score. The experiment using the simulated data proves the effectiveness of the proposed algorithm.

Collaborative filtering based service ranking with invocation histories

Collaborative filtering based recommender systems have been very successful on dealing with the information overload problem and providing personalized recommendations to users. When more and more web services are published online, this technique can also help recommend and select services which satisfy users’ particular Quality of Service (QoS) requirements and preferences. In this thesis, we propose a novel collaborative filtering based service ranking mechanism, in which the invocation and query histories are used to infer the users’ preferences, and user similarity is calculated based on invocations and queries. To overcome some of the inherent problems with the collaborative filtering systems such as the cold start and data sparsity problem, the final ranking score is a combination of the QoS-based matching score and the collaborative filtering based score. The experiment using the simulated data proves the effectiveness of the proposed algorithm.

METHOD OF RANKING ON INFORMATION REFERENCE SITES

The article proposes the application of the method of objective ranking of enterprises on reference sites. The reason for the increase in the number of reference sites of enterprises is investigated. The urgent task of a modern reference portal is highlighted, which is to develop methods and information and communication tools for determining and controlling the number of responses and the overall assessment of the company, timely provision of necessary information and application processing. The main existing shortcomings of service platforms that provide the organization of the interface and communication between the client and the supplier are investigated. An analysis of recent research and publications. The works of scientists are presented, in which the methods of forming the market of rating services, ranking of enterprises and organizations of different spheres of activity were studied. Modern methods of web resources analysis are considered: SEO-audit, analysis based on web analytics, expert evaluation, ranking of organizations according to administrative data. The ranking mechanism is considered. The principles of operation of the all-Ukrainian reference site «TOP 20», which is developing rapidly and requires the introduction of analytical tools to increase its competitiveness among other reference sites. The structure of the site «TOP 20» is given. The information-analytical subsystem of the site is selected, which is one of the components and ranks companies to establish their rating. The difference between reference sites, which is the principle of ranking companies, is studied. To rank companies on the «TOP 20» website, it is proposed to use the formula for calculating the Elo chess rating. The formula for calculating the chess rating of Elo is considered. A modification of the Elo rating formula, which uses an integrated indicator, is proposed. The integrated indicator consists of: the number of user reviews; general assessment of the company; package of services purchased by the company; deadline for registration on the site. The components of the integrated rating on the «TOP 20» website are considered. The application of calculating the integrated rating of companies on the site «TOP 20», which is based on the Elo rating, is considered.

Machine learning application to predict the efficiency of water coning prevention

The increase in number of the mature fields is accompanied by an increase in the water cut of the produced fluids. One of the most common causes of this phenomenon is the process of water coning, that is, the breakthrough of the bottom water to the wellbore, in which water flows form a figure similar to a cone. The paper proposes a ranking mechanism based on machine learning methods that allow to significantly reduce the resource intensity of existing prediction models. In order to preserve the simplicity of presentation, the proposed mechanism is considered on the example of one technology - DWL. Obtained results show about 10% smaller deviation values when using the least squares support vector machine in comparison with the ANN. Both developed models demonstrated acceptable results for practical application.

ACADEMIC RANKING - PREREQUISITE FOR IMPLEMENTING QUALITY MANAGEMENT SYSTEM IN HIGHER EDUCATION INSTITUTIONS

The article aims to substantiate the concept that one of the key prerequisites for implementing quality management system in higher education institutions and making productive management decisions for updating higher education in Ukraine is the formation of an objective ranking mechanism. The challenges and risks in higher education in the case of ignoring the ranking mechanism applied to measure the quality, performance or effectiveness of higher education have been identified.The study subject is the analysis of ranking as a criterion tool for managing higher education institutions. The purpose of the article is to substantiate the importance of ranking for building an effective management system for higher education institutions. The basic study methods are systemic and comparative analysis – systemic tools reveal the importance of ranking as one of the key elements in the management system of higher education institutions, and the comparative method verifies the interdependence of ranking indicators and quality of educational services. Research findings indicate the need for continuous improvement for ranking mechanisms applied to measure the quality, performance or effectiveness of higher education. In the era of globalization, the ranking of higher education institutions is becoming an important tool for assessing the quality of higher education; it serves to mobilize the university function as the locomotive of social development. The research findings can be used to improve the ranking mechanisms and create an effective management system for higher education institutions. The findings help to draw correlations between the effectiveness of ranking tools and the effectiveness of the management system for higher education institutions.Keywords: ranking; ranking tools for assessing the quality of higher education; criteria; correlative dependence; system connections; prospects for modernization of higher education in Ukraine.

Position Ranking and Auctions for Online Marketplaces

Online e-commerce platforms, such as Amazon and Taobao, connect thousands of sellers and consumers every day. In this work, we study how such platforms should rank products displayed to consumers and utilize the top and most salient slots. We present a model that considers consumers’ search costs and the externalities sellers impose on each other. This model allows us to study a multiobjective optimization, whose objective includes consumer and seller surplus as well as the sales revenue, and derive the optimal ranking decision. In addition, we propose a surplus-ordered ranking mechanism for selling some of the top slots. This mechanism is motivated in part by Amazon’s sponsored search program. We show that the Vickrey–Clarke–Groves mechanism would not be applicable to our setting and propose a new mechanism. This mechanism is near optimal, performing significantly better than those that do not incentivize sellers to reveal their private information regarding each consumer purchase, such as their profit. Moreover, we generalize our model to settings in which platforms can provide partial information about the products and facilitate the consumer search and show the robustness of our findings. This paper was accepted by David Simchi-Levi, operations management.

Incremental Refinement of Page Ranking of Web Pages

Web-based information retrieval systems called search engines have made things easy for information seekers, but still do not provide guarantees about the relevance of the information provided to the users. Information retrieval systems provide the information to the user based on certain retrieval criteria. Due to the large size of the WWW, it is very common that a large number of documents get identified related to a particular domain. Therefore, to help users towards finding the best matching documents, a ranking mechanism is employed by the search engine. In this article, an improved architecture for an information retrieval system is proposed. The proposed system makes a query log for each user query and stores the results retrieved to the user for that query. The system also provides relevant results by analyzing the content of the pages retrieved for the user query.