Probability model of sensitive similarity measures in information retrieval. (28th January 2020)

• Record Type: Journal Article
• Authors: Gu, Xiaolong; Zhang, Jie
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 1(2020:Jan./Feb.)
• Publication Date: 2020-01-28
• Subjects: Information retrieval -- network technology -- similarity measure -- probability model -- spectral clustering algorithm; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420901425
• Languages: English
• ISSNs: 1729-8806

Abstract

In today's Internet age, a lot of data is stored and used, which is very important. In people's daily life, if these data are sorted, information retrieval technology will be used, and in information retrieval, some information retrieval inaccuracies often appear. Information retrieval model is an important framework and method for fast, complete, and accurate user information retrieval. With the rapid development of information technology, great changes have taken place in people's production and life. Various information network technologies are widely used in people's lives. The resulting flow of information shows explosive growth, information retrieval. User requirements are getting higher and higher. How to complete personalized information retrieval in a large amount of mixed information, so that retrieval technology can help us obtain effective retrieval results, has become a realistic problem worth exploring. In this article, the application of probability model based on sensitive similarity measure in information retrieval model is analyzed, and a similarity measure algorithm based on spectral clustering is proposed. By improving the similarity measure, the sensitivity problem of scale parameters is overcome and the retrieval precision is improved. In order to better reflect the superiority of the proposed algorithm, this article compares with ng-jordan-weiss (NJW) and deep sparse subspace clustering (DSSC) algorithms. The experimental results show that the proposed algorithm is superior to NJW and DSSC algorithms for different data sets in different evaluation indicators (Rand and F-measure).