  In this paper we address the following problem in web document and
information retrieval (IR): How can we use long-term context information to
gain better IR performance? Unlike common IR methods that use bag of words
representation for queries and documents, we treat them as a sequence of words
and use long short term memory (LSTM) to capture contextual dependencies. To
the best of our knowledge, this is the first time that LSTM is applied to
information retrieval tasks. Unlike training traditional LSTMs, the training
strategy is different due to the special nature of information retrieval
problem. Experimental evaluation on an IR task derived from the Bing web search
demonstrates the ability of the proposed method in addressing both lexical
mismatch and long-term context modelling issues, thereby, significantly
outperforming existing state of the art methods for web document retrieval
task.
