Hierarchical Attention Network for Context-Aware Query Suggestion.

Query suggestion helps search users to efficiently express their information needs and has attracted many studies. Among the different kinds of factors that help improve query suggestion performance, user behavior information is commonly used because user’s information needs are implicitly expressed in their behavior log. However, most existing approaches focus on the exploration of previously issued queries without taking the content of clicked documents into consideration. Since many search queries are short, vague and sometimes ambiguous, these existing solutions suffer from user intent mismatch. To articulate user’s complex information needs behind the queries, we propose a hierarchical attention network which models users’ entire search interaction process for query suggestion. It is found that by incorporating the content of clicked documents, our model can suggest better queries which satisfy users’ information needs. Moreover, two levels of attention mechanisms are adopted at both word-level and session-level, which enable it to attend to important content when inferring user information needs. Experimental results based on a large-scale query log from a commercial search engine demonstrate the effectiveness of the proposed framework. In addition, the visualization of the attention layers also illustrates that informative words and important queries can be captured.