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PDA: Causal Intervention for Leveraging Popularity Bias in Recommendation. Xiangnan He et al. SIGIR, 2021. (PDF) (Source) (Code) (Slides) (Citations 217)
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TIDE: Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation. Xiangfan He et al. TKDE, 2023. (PDF) (Source) (Code) (Citations 30)
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MF-IPS: Recommendations as Treatments: Debiasing Learning and Evaluation. Schnabel T, Swaminathan A, Singh A, et al. ICML, 2016. (PDF)(Source)(Code)(Citation 638)
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DICE: Disentangling user interest and conformity for recommendation with causal embedding. Zheng Y, Gao C, Li X, et al. WWW, 2021. (PDF)(Source)(Code)(Citation 227)
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MACR: Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. Wei T, Feng F, Chen J, et al. KDD, 2021. (PDF)(Source)(Code)(Citation 202)
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CauseE: Causal embeddings for recommendation. Bonner S, Vasile F. RecSys, 2018. (PDF)(Source)(Code)(Citation 245)
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DCCL：Disentangled Causal Embedding With Contrastive Learning For Recommender System（快手 2023）
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DCRS：Disentangled Representation for Diversified Recommendations（字节 2023）
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Self-supervised Learning for Large-scale Item Recommendations（Google 2020）
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ESAM：discriminative domain adaptation with non-displayed items to improve long-tail performance（Alibaba 2020）
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Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders（Alibaba 2022）
- On the Generalizability and Predictability of Recommender Systems. NeurIPS, 2022.
- Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?
- Take a Fresh Look at Recommender Systems from an Evaluation Standpoint
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