  When using the electronic map, POI retrieval is the initial and important
step, whose quality directly affects the user experience. Similarity between
user query and POI information is the most critical feature in POI retrieval.
An accurate similarity calculation is challenging since the mismatch between a
query and a retrieval text may exist in the case of a mistyped query or an
alias inquiry. In this paper, we propose a POI latent semantic model based on
deep networks, which can effectively extract query features and POI information
features for the similarity calculation. Our model describes the semantic
information of complex texts at multiple layers, and achieves multi-field
matches by modeling POI's name and detailed address respectively. Our model is
evaluated by the POI retrieval ranking datasets, including the labeled data of
relevance and real-world user click data in POI retrieval. Results show that
our model significantly outperforms our competitors in POI retrieval ranking
tasks. The proposed algorithm has become a critical component of an online
system serving millions of people everyday.
