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LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops
RFL means that recommender system can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback.
Description of LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops
GitHub Link
The GitHub link is https://github.com/xuchen0427/ltp-mmfIntroduce
This GitHub repository, "XuChen0427/LTP-MMF," contains the implementation of LTP-MMF for SIGIR 2023. The code is intended for use in TOIS and provides the steam dataset, while other datasets can be downloaded from the URLs mentioned in the paper. The implementation is simulated with 256 users and 100 epochs due to space limitations. Users are advised to adjust parameters for different settings. To execute the code, run "python run_LTP-MMF.py." The reported results include a final NDCG of 0.648, MMF of 0.502, and CTR of 0.555. RFL means that recommender system can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback.Content
The implementation of LTP-MMF in TOIS, no other use of the code is allowed! We here only provide steam dataset, other dataset please download them from the urls in the paepr Note that due to the limitation space of anonymous.4open.science, we only simulate it using 256 users and 100 epochs, please modify the parameters for other settings The result is: final NDCG:0.648 MMF:0.502 CTR:0.555
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