The top-ranked documents from various information sources that are merged together into a unified ranked list may cover the same piece of relevant information, and cannot satisfy different user needs. Result diversification(RD) solves this problem by diversifying results to cover more information needs. In recent times, RD has attracted much attention as a means of increasing user satisfaction in general purpose search engines. A myriad of approaches have been proposed in the related works for the diversification problem. However, no concrete study of search result diversification has been done in a Distributed Information Retrieval(DIR) setting. In this paper, we survey, classify and propose a theoretical framework that aims at improving diversification at the result merging phase of a DIR environment.
Ghanaian universities have employed various marketing mix to stimulate demand for their services; particularly in recruitment and engagement of home and international students. This paper examines the relative influence of factors affecting the college choice decisions of students. It is based on a survey of 2,534 admitted students at a major university in Ghana, to which 39 percent of the sample responded. We used Factor analysis to rate 26 University borne attributes upon which student decisions are based. The outcome was used to build a five-scale preference measure, which were evaluated with a qualitative approach in analyzing gathered data using descriptive statistics, cross tabulation and chi square. The results revealed some marketing strategies that can be used in institutions of higher learning to boost students’ enrolment in their order of perceived effectiveness. The following were found to influence decisions: word of mouth (family and friends), proximity/nearness, accreditation/affiliation, affordability and lecturing Style.
Particle swarm optimization (PSO) is a heuristic global optimization method. PSO was motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and the ability to adapt various dynamic environments, makes PSO one of the most important swarm intelligence algorithms and ostensibly the most commonly used optimization technique. This survey presents a comprehensive investigation of PSO and in particular, a proposed theoretical framework to improve its implementation. We hope that this survey would be beneficial to researchers studying PSO algorithms and would also serve as the substratum for future research in the study area, particularly those pursuing their career in artificial intelligence. In the end, some important conclusions and possible research directions of PSO that need to be studied in the future are proposed.
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