DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks

KDD, pp. 1295-1304, 2016.

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We have proposed an attention based pooling on top of recurrent neural networks to model queries and ads in online advertising

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In this paper, we investigate the use of recurrent neural networks (RNNs) in the context of search-based online advertising. We use RNNs to map both queries and ads to real valued vectors, with which the relevance of a given (query, ad) pair can be easily computed. On top of the RNN, we propose a novel attention network, which learns to a...更多

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