Of sorcerers and thought leaders: marketing the information revolution in the 1960s

Cummings, Alex Sayf

doi:10.1080/17541328.2015.1114746

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was information technology and led to the information revolution. 26 It is in the context of the rapid development of computer technology that computer retrieval technology is ushered in.…”

Section: Information Retrieval Technologymentioning

confidence: 99%

Probability model of sensitive similarity measures in information retrieval

Zhang

2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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).

show abstract

Section: Information Retrieval Technologymentioning

confidence: 99%