Media.Net: Difference between revisions

No edit summary
Applied fix pattern(s): Page break normalization
 
(4 intermediate revisions by 3 users not shown)
Line 1: Line 1:
{{CompanyInfo|
{{Organization|
| logo            = Media.netLogo.gif
|city =
| type            = Private
|country = India
| industry        = Internet Advertisement
|date_founded =  
| founded        =  
|facebook =  
| founders       = [[Divyank Turakhia]]
|focus = Internet Advertisement
| ownership      = [[Directi]]
|founders = [[Divyank Turakhia]]
| headquarters    =
|linkedin = [http://www.linkedin.com/company/media.net Media.net]
| country        = India
|logo = Media.netLogo.gif
| businesses      =
|organization_type = Private
| products        =
|ownership = [[Directi]]
| employees      = 501-1000 employees
|subsidiaries =
| revenue        =
|website =
| email          =
|x =
| website        =
| blog            =
| facebook        =
| linkedin       = [http://www.linkedin.com/company/media.net Media.net]
| twitter        =  
| keypeople      = [[Divyank Turakhia]], CEO<br>
[[Namit Merchant]], COO<br>
[[Vaibhav Arya]], CTO<br>
[[Andrew Allemann]], Sr. Vice President<br>
[[Vishal Manjalani]], Vice President
}}
}}
'''Media.net''' is an Internet advertising company that uses complex superior and classification algorithms to identify and deliver the most contextually relevant ads for a web page. The company claims to have the most efficient system, which produces hybrid ads in real time. Real time ads are those generated according to the interests of each visitor. This understanding is achieved by techniques of Machine Learning, Data Mining, Linguistic Analysis and Advanced Statistics.<ref>[http://www.media.net/careers/ Careers at Media.net]</ref>  
'''Media.net''' is an Internet advertising company that uses complex superior and classification algorithms to identify and deliver the most contextually relevant ads for a web page. The company claims to have the most efficient system, which produces hybrid ads in real time. Real time ads are those generated according to the interests of each visitor. This understanding is achieved by techniques of Machine Learning, Data Mining, Linguistic Analysis and Advanced Statistics.<ref>[http://www.media.net/careers/ Careers at Media.net]</ref>  
Line 36: Line 26:
{{reflist}}
{{reflist}}


__NOTOC__
[[Category:Communications/Marketing]]
[[Category:Companies|Media.net]]