🎉 Lab Celebration: Eran’s RecSys 2025 Paper!

We are proud to share that our PhD student Eran Fainman, co-advised by Dr. Adir Solomon, has had his paper accepted to the ACM Conference on Recommender Systems (RecSys 2025) in Prague.

The paper, “PAIRSAT: Integrating Preference-Based Signals for User Satisfaction Estimation in Dialogue Systems,” tackles a central challenge in conversational AI: how to measure user satisfaction in a way that is both accurate and scalable.

Most current systems rely on explicit satisfaction labels (“satisfied”, “neutral”, “dissatisfied”), but these are expensive to collect and often domain-specific. Eran’s work introduces PAIRSAT, a new model that combines these scarce labels with the abundant preference data generated when users choose one AI response over another. By reframing satisfaction prediction as a bounded regression task and integrating pairwise ranking loss, PAIRSAT is able to capture both the nuance of absolute labels and the richness of relative feedback.

The results are compelling: across multiple datasets, PAIRSAT demonstrates strong and robust performance, showing that preference data can be a powerful complement to traditional labels.

This work is part of our lab’s broader agenda on human-centered AI and recommender systems, and we are delighted to see Eran’s contributions recognized by the RecSys community.

👏 Congratulations to Eran on this achievement — and thank you to Adir and the team for their support and collaboration.

👉 Read the paper here: ACM RecSys 2025 Proceedings