Paper Info
Title
DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks
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.
Year
DOI
Venue
2016
10.1145/2939672.2939759
KDD
Keywords
Field
DocType
Deep Learning,RNN,Attention Mechanism,Online Advertising,Sponsored Search,Query Rewriting,BM25
Query Rewriting,Data mining,Search engine,Computer science,Recurrent neural network,Online advertising,Artificial intelligence,Deep learning,Machine learning
Conference
Citations 
PageRank 
References 
27
0.94
16
Authors
4
Name
Order
Citations
PageRank
shuangfei zhai19910.00
Keng-hao Chang2282.65
Ruofei Zhang344229.57
Zhongfei (Mark) Zhang42451164.30