Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion

The query logs from an on-line map query system provide rich cues to understand the behaviors of human crowds. With the growing ability of collecting large scale query logs, the query suggestion has been a topic of recent interest. In general, query suggestion aims at recommending a list of relevant queries w.r.t. users’ inputs via an appropriate learning of crowds’ query logs. In this paper, we are particularly interested in map query suggestions (e.g., the predictions of location-related queries) and propose a novel model
Hierarchical Contextual Attention Recurrent Neural Network
(HCAR-NN) for map query suggestion in an encoding-decoding manner. Given crowds map query logs, our proposed HCAR-NN not only learns the local temporal correlation among map queries in a query session (e.g., queries in a short-term interval are relevant to accomplish a search mission), but also captures the global longer range contextual dependencies among map query sessions in query logs (e.g., how a sequence of queries within a short-term interval has an influence on another sequence of queries). We evaluate our approach over millions of queries from a commercial search engine (i.e.,
Baidu Map
). Experimental results show that the proposed approach provides significant performance improvements over the competitive existing methods in terms of classical metrics (i.e.,
Recall@K
and
MRR
) as well as the prediction of crowds’ search missions.