G. Dimitrov, G. Panayotova, I. Garvanov, Bychkov Os, Pavel Petrov, Angel Angelov
{"title":"Performance analysis of the method for social search of information in university information systems","authors":"G. Dimitrov, G. Panayotova, I. Garvanov, Bychkov Os, Pavel Petrov, Angel Angelov","doi":"10.1109/ICAIPR.2016.7585228","DOIUrl":null,"url":null,"abstract":"In this article the effectiveness of one of the main methods for assisting the information search in large data sets is analyzed, namely the method based on the social approach. This method analyzes the behavior of multiple users when searching for information, particularly the keywords they use. Based on the obtained results, the relevant algorithms in the searching engines of the organizations can be optimized. The studies are based on data extracted from the databases of two relatively large information systems based at the University in Library Studies and Information Technologies in Sofia, Bulgaria and University of Economics in Varna, Bulgaria. The numerical and experimental results are analyzed and discussed.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"593 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIPR.2016.7585228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
In this article the effectiveness of one of the main methods for assisting the information search in large data sets is analyzed, namely the method based on the social approach. This method analyzes the behavior of multiple users when searching for information, particularly the keywords they use. Based on the obtained results, the relevant algorithms in the searching engines of the organizations can be optimized. The studies are based on data extracted from the databases of two relatively large information systems based at the University in Library Studies and Information Technologies in Sofia, Bulgaria and University of Economics in Varna, Bulgaria. The numerical and experimental results are analyzed and discussed.