  Query suggestions help users of a search engine to refine their queries.
Previous work on query suggestion has mainly focused on incorporating directly
observable features such as query co-occurrence and semantic similarity. The
structure of such features is often set manually, as a result of which hidden
dependencies between queries and users may be ignored. We propose an AHNQS
model that combines a hierarchical structure with a session-level neural
network and a user-level neural network to model the short- and long-term
search history of a user. An attention mechanism is used to capture user
preferences. We quantify the improvements of AHNQS over state-of-the-art
RNN-based query suggestion baselines on the AOL query log dataset, with
improvements of up to 21.86% and 22.99% in terms of MRR@10 and Recall@10,
respectively, over the state-of-the-art; improvements are especially large for
short sessions.
