Hierarchical Recurrent Encoder-Decoder code (HRED) for Query Suggestion.
This code accompanies the paper:
"A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion", by Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob G. Simonsen, Jian-Yun Nie, to appear in CIKM'15.
The pre-print of the paper is available at: http://arxiv.org/abs/1507.02221.
-- Data processing
The dataset must consist in two files:
data.ses: each line is a sequence of tab-separated strings (queries). Each line represents a query session. data.rnk: each line is a sequence of tab-separated integers (not currently used in the model, can be set to a tab-separated list of 0).
Basically, the .rnk file is not used by the model but it contains the rank of the clicked documents for each of the queries.
This will create the preprocessed dataset for training.
Create a prototype by modifying state.py and launch:
python train.py --prototype your_prototype