Meysam Varasteh

PhD Student in Computer Science
Recommender Systems · Explainable AI · Interpretability
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About Me

I am a PhD student in Computer Science at the University of Colorado Boulder. My research focuses on recommender systems, explainability, and interpretability.

I am particularly interested in understanding how recommendation algorithms make decisions and how these decisions affect different stakeholders. A major direction of my research is transparency in recommender systems, especially from the perspective of item and content providers.

More broadly, I am interested in interpretable machine learning, explainable AI, human-centered recommender systems, and responsible recommendation.

Research Interests

Recommender Systems Explainable AI Interpretability Transparency Provider-Side Recommendation Contrastive Explanations

Selected Research

Recommendation Transparency for Providers

We study how item providers can better understand why their content receives more or less exposure in recommender systems. Our work develops system-level explanations for understanding the factors driving recommendation exposure.

Paper Code arXiv

Contrastive Explanations for Recommender Systems

We investigate contrastive explanations for recommender systems, focusing on questions such as why one recommendation outcome occurred instead of another.

Paper Code

Publications

Why Didn't More People See It? Recommendation Transparency for Provider Stakeholders
Meysam Varasteh, et al.
IntRS Workshop @ ACM RecSys 2026
Paper arXiv Code
Your Second Publication Title
Meysam Varasteh, Collaborator Name, et al.
Conference / Workshop, 2026
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CV

You can view my current curriculum vitae below.

View CV

Contact

I am interested in research collaborations and opportunities related to recommender systems, explainability, and interpretable machine learning.

Email: YOUR_EMAIL@colorado.edu

GitHub: github.com/meysamvarasteh92