A
Ashvin Kannan
Researcher at LinkedIn
Publications - 25
Citations - 638
Ashvin Kannan is an academic researcher from LinkedIn. The author has contributed to research in topics: Advertising campaign & Common value auction. The author has an hindex of 13, co-authored 25 publications receiving 637 citations. Previous affiliations of Ashvin Kannan include Yahoo! & Nuance Communications.
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Patent
Adaptation of a speech recognition system across multiple remote sessions with a speaker
Hy Murveit,Ashvin Kannan +1 more
TL;DR: In this article, a technique for adaptation of a speech recognizing system across multiple remote communication sessions with a speaker is presented. But, the technique requires the speaker to engage in a training session.
Patent
System and method for positioning sponsored content in a social network interface
TL;DR: In this paper, a system and method optionally includes or utilizes a processor may receive a request for social network content for display in a position of a plurality of sponsored content positions in a newsfeed of a social network interface.
Patent
Voice interface to a social networking service
TL;DR: In this article, a machine may be configured to generate and provide, for example, a voice-user interface to a social networking service, which enables a member of the social network to access member profile information for other members of the network.
Patent
Speech recognition system to selectively utilize different speech recognition techniques over multiple speech recognition passes
TL;DR: In this paper, a multi-pass speech recognition system was proposed, in which a processor performs a first pass speech recognition technique on the spoken input and forms first pass results, each having an assigned score related to the certainty that the corresponding expression correctly matches the input.
Patent
System and method for generating functions to predict the clickability of advertisements
Chad Carson,Ashvin Kannan,Erick Cantú-Paz,Rukmini Iyer,Pero Subasic,Christopher LuVogt,Christopher John Leggetter,Jan Pedersen,David Ku +8 more
TL;DR: In this article, the authors present a method for predicting a frequency with which an advertisement displayed in response to a query will be selected using the analytics data and features associated with the one or more advertisements displayed by the advertisers.