Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning

Hao Wang, Zhichao Chen, Haotian Wang, Yanchao Tan, Licheng Pan, Tianqiao Liu, Xu Chen, Haoxuan Li, Zhouchen Lin
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:62575-62595, 2025.

Abstract

Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples. This assumption risks misclassifying potential positive samples within the unlabeled data, thereby undermining model performance. To address this issue, we introduce PURL, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior. To address this challenge, we propose Progressive Proximal Transport (PPT), which estimates the class prior by minimizing the proximal transport cost between positive and unlabeled samples. Experiments on three real-world datasets validate the efficacy of PURL in terms of improved recommendation quality. Code is available at https://github.com/HowardZJU/weakrec.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-wang25p, title = {Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning}, author = {Wang, Hao and Chen, Zhichao and Wang, Haotian and Tan, Yanchao and Pan, Licheng and Liu, Tianqiao and Chen, Xu and Li, Haoxuan and Lin, Zhouchen}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {62575--62595}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/wang25p/wang25p.pdf}, url = {https://proceedings.mlr.press/v267/wang25p.html}, abstract = {Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples. This assumption risks misclassifying potential positive samples within the unlabeled data, thereby undermining model performance. To address this issue, we introduce PURL, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior. To address this challenge, we propose Progressive Proximal Transport (PPT), which estimates the class prior by minimizing the proximal transport cost between positive and unlabeled samples. Experiments on three real-world datasets validate the efficacy of PURL in terms of improved recommendation quality. Code is available at https://github.com/HowardZJU/weakrec.} }
Endnote
%0 Conference Paper %T Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning %A Hao Wang %A Zhichao Chen %A Haotian Wang %A Yanchao Tan %A Licheng Pan %A Tianqiao Liu %A Xu Chen %A Haoxuan Li %A Zhouchen Lin %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-wang25p %I PMLR %P 62575--62595 %U https://proceedings.mlr.press/v267/wang25p.html %V 267 %X Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods often resort to negative sampling, a technique that assumes unlabeled interactions as negative samples. This assumption risks misclassifying potential positive samples within the unlabeled data, thereby undermining model performance. To address this issue, we introduce PURL, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior. To address this challenge, we propose Progressive Proximal Transport (PPT), which estimates the class prior by minimizing the proximal transport cost between positive and unlabeled samples. Experiments on three real-world datasets validate the efficacy of PURL in terms of improved recommendation quality. Code is available at https://github.com/HowardZJU/weakrec.
APA
Wang, H., Chen, Z., Wang, H., Tan, Y., Pan, L., Liu, T., Chen, X., Li, H. & Lin, Z.. (2025). Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:62575-62595 Available from https://proceedings.mlr.press/v267/wang25p.html.

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