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uRank_uMart

Listwise Learning to Rank by Exploring Unique Ratings (accepted at WSDM 2020)

Configuration

Set a system variable RAW_RANK_DATA that stores the folder that contains the raw ranking data

Set a system variable TF_RANK_DATA that stores the folder that contains the serialized ranking data

Convert raw ranking data to tf records (THIS IS AN IMPORTANT STEP)

python prepare_data.py

Your TF_RANK_DATA should now contain the serialized tf records for running the experiments.

You will see the batch size being 1, meaning one serialized query per batch. The actual batch size the number of documents per query.

Thanks to suggestions from WSDM attendants. An engineering side improvement could be accumulating the loss every a few batches to receive more smooth gradient updates.

params

src/experiments/base_model/params.json contains all of the hyper-parameters.

For instance, num_learners is used to control how many weak learners are used in uBoost and urBoost.

mlp_sizes controls the number of MLP layers and the number of neurons per layer.

residual_mlp_sizes controls the MLP network parameters for the gradient boosting step.

pooling and rnn are used for urRank and urBoost only. "pooling": "MP" denotes max-pooling, and "pooling": "AP" denotes average-pooling. "rnn": "C1" generally works better than "rnn": "C2".

batch_size needs to be 1, which indicates one query per batch or we put say all query-document features and labels that belong to the same query in the same batch.

uRank

Set num_learners to 1. Change the GPU number accordingly.

./run_OHSUMED_uRank.sh

uBoost

Set num_learners to an integer larger than 1.

./run_OHSUMED_uRank.sh

urRank

This model was not mentioned in the paper. It is uRank + RNN without boosting.

./run_OHSUMED_urRank.sh

urBoost

Set num_learners to an integer larger than 1.

./run_OHSUMED_urBoost.sh

uMart

Set a system variable LIGHTGBM_DATA that stores the folder that contains the LightGBM ranking data

Convert the raw learning-to-rank data sets to LightGBM inputs.

python msltr2libsvm.py

Replace the rank_objective.hpp in this repo with the same file in LightGBM (https://github.com/microsoft/LightGBM) and compile to obtain the binary for training uMart.

Notice

Please keep in mind that the default NDCG calculation in LightGBM takes queries with all 0 labels as NDCG any position 1, i.e., NDCG@1=1,  NDCG@3=1, NDCG@5=1, NDCG@10=1.

The original NDCG script used for MQ2007 and MQ2008 data sets take such case as 0 (see mslr-eval-score-mslr-original.pl).

Yahoo learning to rank dataset takes such case as 0.5.

We remove queries with all 0 labels from all of the data sets to avoid this confusion. This was done during python prepare_data.py.

We fixed the logic and added ERR calculation based on the perl script (see mslr-eval-score-mslr.pl).

We also implemented ranknet, listnet, listmle in this repo, however, it might not be as efficient as in TF-Ranking https://github.com/tensorflow/ranking. Other implementations can be found at https://github.com/microsoft/LightGBM and https://sourceforge.net/p/lemur/wiki/RankLib/.

We will release a Tensorflow 2 version in 2021.

Citation

Please kindly cite our work if you would like to use our code.

@inproceedings{10.1145/3336191.3371814, author = {Zhu, Xiaofeng and Klabjan, Diego}, title = {Listwise Learning to Rank by Exploring Unique Ratings}, year = {2020}, isbn = {9781450368223}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3336191.3371814}, doi = {10.1145/3336191.3371814}, booktitle = {Proceedings of the 13th International Conference on Web Search and Data Mining}, pages = {798–806}, numpages = {9}, keywords = {listwise learning to rank, learning to rank, deep learning, recurrent neural network}, location = {Houston, TX, USA}, series = {WSDM ’20} }

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