A hierarchical recurrent encoder-decoder for generative context-aware query suggestion

TY - GEN

T1 - A hierarchical recurrent encoder-decoder for generative context-aware query suggestion

AU - Sordoni, Alessandro

AU - Bengio, Yoshua

AU - Vahabi, Hossein

AU - Lioma, Christina

AU - Simonsen, Jakob Grue

AU - Nie, Jian-Yun

PY - 2015/10/17

Y1 - 2015/10/17

N2 - Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a novel hierarchical recurrent encoder-decoder architecture that makes possible to account for sequences of previous queries of arbitrary lengths. As a result, our suggestions are sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that our model outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our architecture is general enough to be used in a variety of other applications.

AB - Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a novel hierarchical recurrent encoder-decoder architecture that makes possible to account for sequences of previous queries of arbitrary lengths. As a result, our suggestions are sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that our model outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our architecture is general enough to be used in a variety of other applications.

U2 - 10.1145/2806416.2806493

DO - 10.1145/2806416.2806493

M3 - Article in proceedings

SP - 553

EP - 562

BT - CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

PB - Association for Computing Machinery

T2 - CIKM 2015

Y2 - 19 October 2015 through 23 October 2015

ER -