Topics

TrustRS invites researchers and practitioners to explore trust challenges spanning the full lifecycle of recommender systems. Topics of interest include, but are not limited to:

Learning

•  Fairness-aware and bias-mitigated recommendation

• Explainable and transparent recommendation models

• Privacy-preserving recommendation via federated learning or differential privacy

• Robustness of recommender systems against adversarial attacks and data poisoning

Relearning

• Continual and incremental learning for evolving user preferences

• Adaptive recommendation under distribution shift and concept drift

• Efficient model updating with fairness and privacy guarantees

Unlearning

• Machine unlearning for traditional recommendation and LLM-based recommendation

• Efficient approximate unlearning in graph-based and sequential recommender systems

• Unlearning verification and auditing in recommender systems

Instructions for Submissions 

Submission instructions, formatting requirements, and paper length limits will be announced soon. 

Paper Submission Site 

The paper submission site will be announced soon.