An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow implementation of SERank model. The code is developed based on TF-Ranking.

Related tags

Deep Learning dnn learning-to-rank sequence-wise
Overview

SERank

An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow implementation of SERank model. The code is developed based on TF-Ranking.

Compared with GSF(Groupwise Scoring Function), our method obtains comparable ranking performance gain, while only requiring little computation overhead.

The SERank model has been suceessfully deployed in Zhihu Search ranking, which is one of the largest Community Question Answering platform in China.

image info

image info

Dependencies

Dataset

The demo dataset in this repo is randomly sampled from MSLR web30k dataset. You may download the whole web30K dataset from Microsoft Learning to Rank Datasets Page and place train.txt, vali.txt, test.txt in the data folder.

Model

Our main idea is to develop a sequencewise model structure which accepts a list of ranking candidates, and jointly score all candidates. We introduce SENet structure into the ranking model, where the basic idea is using SENet structure to compute feature importance according the the context of ranking list. image info

For the detail about the model structure, you may refer to our paper published on arxiv.

How to Train

bash run_train.sh

Citation

SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network. [arxiv]

@article{wang2020serank,
  title={SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network},
  author={Wang, RuiXing and Fang, Kuan and Zhou, RiKang and Shen, Zhan and Fan, LiWen},
  journal={arXiv preprint arXiv:2006.04084},
  year={2020}
}
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Comments
  • problem about Recurrence experiment in Web30k

    problem about Recurrence experiment in Web30k

    Hi, thx for your great job. but I have problem about recurrence experiment in web30k. I download the code, and change data to web30k, ndcg@5 of the last ckpt is only 0.409, which is 0.456 in paper. I do not change serank.py. and the run_script is

    DATA=/path-to-web30k/Fold1

output_dir=outputs
rm -r $output_dir
mkdir $output_dir

python serank.py \
  --train_path=$DATA/train.txt \
  --vali_path=$DATA/vali.txt \
  --test_path=$DATA/test.txt \
  --output_dir=$output_dir \
  --num_features=136 \
  --serank=True \
  --query_label_weight=True

    tail of the train log is

    I0322 18:19:51.462585 140648353728320 evaluation.py:276] Finished evaluation at 2021-03-22-18:19:51
INFO:tensorflow:Saving dict for global step 100000: global_step = 100000, labels_mean = 0.66818184, logits_mean = -0.27165264, loss = 258.99927, metric/arp = 38.71134, metric/ndcg@1 = 0.38938853, metric/ndcg@10 = 0.43623617, metric/ndcg@3 = 0.39571184, metric/ndcg@5 = 0.40858307, metric/ordered_pair_accuracy = 0.6484862
I0322 18:19:51.462839 140648353728320 estimator.py:2066] Saving dict for global step 100000: global_step = 100000, labels_mean = 0.66818184, logits_mean = -0.27165264, loss = 258.99927, metric/arp = 38.71134, metric/ndcg@1 = 0.38938853, metric/ndcg@10 = 0.43623617, metric/ndcg@3 = 0.39571184, metric/ndcg@5 = 0.40858307, metric/ordered_pair_accuracy = 0.6484862
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100000: outputs/model.ckpt-100000
I0322 18:19:51.463721 140648353728320 estimator.py:2127] Saving 'checkpoint_path' summary for global step 100000: outputs/model.ckpt-100000

    thx for your help.

    enhancement 
    opened by stanpcf 4
Owner
Zhihu
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Zhihu
GitHub https://arxiv.org/abs/2006.04084
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