To replicate this work just install the requirements.txt in your python environment.
python==3.8.0 is required.
e.g.
using pip
pip install -r requirements.txt
using Conda
conda create --name <env_name> --file requirements.txt python=3.8.0
The relevant papers for this experiment are:
- A probabilistic method for inferring preferences from clicks: https://dl.acm.org/doi/10.1145/2063576.2063618
- Comparing the Sensitivity of Information Retrieval Metrics: https://dl.acm.org/doi/10.1145/1835449.1835560
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The <query,document> judgements dataset used in this paper is /MSLR-WEB30k/Fold1/train.txt
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To download the data/MSLR-WEB30k/Fold1 please refer to: https://www.microsoft.com/en-us/research/project/mslr
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The dataset is passed in the experiment as 'dataset_path' parameter
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The long tail distribution is extracted for real-world query logs and stored here: data/long_tail/query_click_user.json
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To reproduce the experiments in this paper please use the provided long tail distribution and pass it as 'long_tail_dataset_path' parameter
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If you want to run the experiments with a custom long tail distribution, feel free to use the same json structure (Apache Solr JSON facets response)
Run 9 and 10 have been executed on Amazon Ec2 machines Z1d 2Xlarge. All other runs have been executed on Amazon Ec2 machines T2 Xlarge. The detailed output of each run is in the runs_output folder.
1:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 234 -q 1000 -m 136 -e 1 -c 10 > runs_output/run-1.txt
2:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 347 -q 1000 -m 136 -e 1 > runs_output/run-2.txt
3-4:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 333 -q 1000 -m 136 -e 1 -n 10 -c 10 > runs_output/run-3-4.txt
5:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 173 -q 100 -m 136 -e 1 -n 10 -c 10 > runs_output/run-5.txt
6:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 193 -q 100 -m 136 -e 1 -n 10 -c 10 -u 10 > runs_output/run-6.txt
7:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 777 -q 100 -m 136 -e 1 -c 10 -u 10 > runs_output/run-7.txt
8:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 33 -q 1000 -m 136 -e 1_long_tail -a data/long-tail-1/query_click_user.json -u 0.02 -n 10 -c 10 > runs_output/run-8.txt
9:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 937 -q 1000 -m 136 -e 1_long_tail -a data/long-tail-1/query_click_user.json -u 0.125 -n 10 -c 10 > runs_output/run-9.txt
10:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 571 -q 1000 -m 70 -e 1_long_tail -a data/long-tail-1/query_click_user.json -u 0.250 -n 10 -c 10 > runs_output/run-10.txt
11:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 133 -q 1000 -m 136 -e 1_long_tail -a data/long-tail-1/query_click_user.json -u 0.02 -c 10 > runs_output/run-11.txt
12:
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 189 -q 1000 -m 136 -e 1_long_tail -a data/long-tail-1/query_click_user.json -u 0.02 -n 10 -c 10 -r True > runs_output/run-12.txt
13
python3 -u ./main.py -p data/MSLR-WEB30k/Fold1/train.txt -s 133 -q 1000 -m 136 -e 1_long_tail -a data/long-tail-1/query_click_user.json -u 0.02 -c 10 -r True > runs_output/run-13.txt