NDLI: DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks

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DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks

Content Provider	ACM Digital Library
Author	Zhang, Ruofei Zhang, Zhongfei Mark Zhai, Shuangfei Chang, Keng-hao
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.
Starting Page	1295
Ending Page	1304
Page Count	10
File Format	PDF
ISBN	9781450342322
DOI	10.1145/2939672.2939759
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deep learning Bm25 Attention mechanism Rnn Query rewriting Online advertising Sponsored search
Content Type	Text
Resource Type	Article

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