We invite contributions ranging from theoretical or conceptual papers to technical algorithmic ones as well as applications and case studies towards the trustworthy recommendation, including but not limited to the following areas:

Fundamental or emerging data science or artificial intelligence theories, approaches, and applications related to trustworthy recommendations
Recommendation with low-quality data, including highly sparse data, noisy or corrupted data, heavily duplicated data, and biased data
Uncertainty modeling for recommendation where user interests frequently drift over time and/or results need to be presented in a highly dynamic environment
Robustness models for recommendations including attacks and counter approaches
Interpretable recommendation that provides persuasive explanations and/or generates faithful interpretations to the recommendation process
Fairness and debiasing, where a fair system is designed to balance its accuracy with potential biases and/or unfairness
Security and privacy-aware recommendations including federated recommendation, on-device training/inference, and privacy-protected ranking mechanisms
Human-in-the-loop computing for improving accuracy, explainability, or adaptivity
Surveys, evaluations, or benchmarking on state-of-the-art research in the area of trustworthy recommendations
Novel and emerging applications of recommendation techniques, especially trustworthiness related approaches and solutions
Novel evaluation protocols, approaches and metrics for evaluating the trustworthiness of recommendation