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Fair-NCL: Fairness-Aware Non-Contrastive Learning with Controlled Augmentation for Sequential Recommendation

https://doi.org/10.2139/ssrn.7206473

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11257-023-09364-zopenalex-snapshot

Fairness in recommender systems: research landscape and future directions

inferredcites →https://doi.org/10.1109/access.2025.3616855openalex-snapshot

Time-Evolving Fairness and Accuracy in Recommendation Systems: A Stereotype-Based Framework for Addressing Trade-Offs

inferredcites →https://doi.org/10.1109/icdm.2018.00035openalex-snapshot

Self-Attentive Sequential Recommendation

inferredcites →https://doi.org/10.1145/3159652.3159656openalex-snapshot

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

inferredcites →https://doi.org/10.1145/3278721.3278779openalex-snapshot

Mitigating Unwanted Biases with Adversarial Learning

inferredcites →https://doi.org/10.1145/3488560.3498375openalex-snapshot

It Is Different When Items Are Older

inferredcites →https://doi.org/10.1145/3488560.3498433openalex-snapshot

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

inferredcites →https://doi.org/10.1145/3543507.3583341openalex-snapshot

Improving Recommendation Fairness via Data Augmentation

inferredcites →https://doi.org/10.1145/3543507.3583361openalex-snapshot

Debiased Contrastive Learning for Sequential Recommendation

inferredcites →https://doi.org/10.1145/3543507.3583538openalex-snapshot

CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation

inferredcites →https://doi.org/10.1145/3581783.3613831openalex-snapshot

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

inferredcites →https://doi.org/10.1145/3589334.3645536openalex-snapshot

Ensuring User-side Fairness in Dynamic Recommender Systems

inferredcites →https://doi.org/10.1145/3610302openalex-snapshot

Fairness in Recommendation: Foundations, Methods, and Applications

inferredcites →https://doi.org/10.1145/3627826openalex-snapshot

Trustworthy Recommender Systems

inferredcites →https://doi.org/10.1145/3664928openalex-snapshot

Fairness and Diversity in Recommender Systems: A Survey

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