Artificial neural network

DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks

• Report rights infringement
• published: 10 Oct 2016
• views: 4

Author: Shuangfei Zhai, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Sciences, Binghamton University, State University of New York Abstract: 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 assign attention scores to different word locations according to their intent importance (hence the name DeepIntent). The vector output of a sequence is thus computed by a weighted sum of the hidden states of the RNN at each word according their attention scores. We perform end-to-end training of both the RNN and attention network under the guidance of user click logs, which are sampled from a commercial search engine. We show that in most cases the attention network improves the quality of learned vector representations, evaluated by AUC on a manually labeled dataset. Moreover, we highlight the effectiveness of the learned attention scores from two aspects: query rewriting and a modified BM25 metric. We show that using the learned attention scores, one is able to produce sub-queries that are of better qualities than those of the state-of-the-art methods. Also, by modifying the term frequency with the attention scores in a standard BM25 formula, one is able to improve its performance evaluated by AUC. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/