A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
This addresses the challenge of data sparsity in providing relevant query suggestions for users of search engines, though it is incremental as it builds on existing encoder-decoder architectures.
The paper tackles the problem of generating context-aware query suggestions for search engines by accounting for sequences of previous queries to preserve search intent, and it results in outperforming existing context-aware approaches in next query prediction.
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 probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths. Our novel hierarchical recurrent encoder-decoder architecture allows the model to be 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 it outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our model is general enough to be used in a variety of other applications.