DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks
KDD, pp. 1295-1304, 2016.
We have proposed an attention based pooling on top of recurrent neural networks to model queries and ads in online advertising
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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