Recommendation systems play a significant role in shaping online experiences and decision-making, making it crucial to develop systems that are trustworthy and transparent. This workshop will focus on enhancing trust in recommendation systems by addressing various technical and ethical challenges. We will explore fairness, explainability, content safety, algorithm transparency, and the broader societal impact of recommendation systems.

Submission Guidelines

We invite submissions of extended abstracts (1-6 pages) that align with the workshop's themes. We encourage contributions in the following areas:

• New theoretical research that advances our understanding of trust in machine learning systems.

• Best practices for evaluating the trustworthiness and safety of large-scale machine learning systems.

• Practical solutions to address trust issues in deployed systems, including case studies and real-world applications.

• Creative applications of new technologies to enhance trust in modern recommendation systems.

Submission guidelines: All submitted papers must

• follow the instructions of WSDM 2025 final submissions;

• be formatted according to the ACM SIG Proceedings Template -double-column format, with a font size no smaller than 9pt;

• be in PDF (make sure that the PDF can be viewed on any platform), and formatted for US Letter size.

Submissions are encouraged to include links or demos as attachments to enhance their presentation. All submissions will undergo a rigorous peer-review process to ensure quality and originality. It is imperative that submitted works are not concurrently under review at any other conference, workshop, or journal and contain original, unpublished contributions. Accepted papers will be published according to the ACM RECSYS 2024 WS publication rules.

Committees

Program Committee

• Xinghai Hu

Organizing committee

• Xinghai Hu
• Xu Liu
• Robin Burke

Contact

xinghai.me@gmail.com