Littérature scientifique sur le sujet « SPEARMAN FOOTRULE DISTANCE »

Rank aggregation is applied on the web to build various applications like meta-search engines, consumer reviews classification, and recommender systems. Meta-searching is the generation of a single list from a collection of the results produced by multiple search engines, together using a rank aggregation technique. It is an efficient and cost-effective technique to retrieve quality results from the internet. The quality of results produced by a meta-searching relies upon the efficiency of rank aggregation technique applied. An effective rank aggregation technique assigns the rank to a document that is closest to all its previous rankings. The newly generated list of documents may be evaluated by the measurement of Spearman footrule distance. In this article, various fuzzy logic techniques for rank aggregation are analyzed and further improvements are proposed in Modified Shimura technique. Consequently, two novel OWA operators are suggested for the calculation of membership values of document ranks in a modified Shimura technique. The performance of proposed improvements is evaluated on the Spearman footrule distance along with execution time. The results show that the anticipated improvements exhibit better performance than other fuzzy techniques.

Multiple rankings of same object based on multiple criteria poses a problem of choice to rank that object at a position closest to all the rankings. Choosing a ranking for a list of such objects ranked previously is called rank aggregation. The aggregated ranking is analysed by computing the Kendall tau distance or Spearman Footrule distance. The ranking chosen by minimizing Spearman Footrule distance, is NP-Hard even if number of ranking lists greater than four for partial lists. In context of World Wide Web, the results generated by the multiple search engines may be collected together and an aggregated result can be produced using rank aggregation methods or in simple words the meta-search engine comes into existence. However, the use of existing search engines reveal that none of them have been effective in generating the results up to the quality and reliability desired by the end user, the reason being many. The application of fuzzy logic techniques to minimize the aggregated Spearman Footrule distance obtained from rank aggregation when applied in meta-searching have been studied at length, and the improvements in the existing Shimura’s technique, viz. Enhanced Shimurasquare and Enhanced Shimura-sqrt have been proposed to achieve the extent that is better to Borda’s Method, a benchmark for comparison of aggregated rankings as well the other common techniques. In addition, the improvements in existing heuristics based positional methods have also been proposed. A new heuristics have also been put forward for future work. A series of experiments on real and benchmark data have been conducted to validate the proposed improvements. The platform used for experimentation was matlab framework. A comparative analysis of the previous and the proposed rank aggregation methods on six search engines and twenty one search queries have been presented. The different aggregation methods have been compared on the basis of aggregate spearman footrule distances. The computation time have also been measured and reported. It has been revealed by the experiments that our proposed soft computing method, Enhanced Shimura Square performs best in terms of effective performance and computational efficiency.

Metasearching is the process of combining search results of different search systems into a single set of ranked results which, in turn, is expected to provide us the collective benefit of using each of the participating search systems. Since, user is the direct beneficiary of the search results; this motivates the researchers in the field of Human Computer Interaction (HCI) to measure user satisfaction. A user is satisfied if he receives good quality search results in response to his query. To measure user satisfaction, we need to obtain feed back from user. This feedback might also be used to improve the quality of metasearching. The authors discuss the design of a metasearch system that is based on human computer interaction. We compare our method with two other methods Borda’s method and modified Shimura technique. The authors use Spearman’s footrule distance as the measure of comparison. Experimentally, the method performs better than the Borda’s method. The authors argue that the method is significant as it models the user feedback based metasearching and has spam-fighting capabilities.