# Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction

arXiv: https://arxiv.org/abs/2402.08976 · HTML: https://mlsanity.com/paper/2402.08976/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2024.

## Built on by (most cited first, 4 of 4)

- [Conformal Prediction: A Data Perspective](https://mlsanity.com/paper/2410.06494/) (2024)
- [Taxonomy-Guided Zero-Shot Recommendations with LLMs](https://mlsanity.com/paper/2406.14043/) (2024)
- [Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks](https://mlsanity.com/paper/2410.21618/) (2024)
- [Reinforcement Learning with Conformal Action Sets: An Application to Sequential Recommendation](https://mlsanity.com/paper/2610.08743/) (2026)

## Abstract (arXiv)

In Sequential Recommendation Systems (SRecsys), traditional training approaches that rely on Cross-Entropy (CE) loss often prioritize accuracy but fail to align well with user satisfaction metrics. CE loss focuses on maximizing the confidence of the ground truth item, which is challenging to achieve universally across all users and sessions. It also overlooks the practical acceptability of ranking the ground truth item within the top-K positions, a common metric in SRecsys. To address this limitation, we propose CPFT, a novel fine-tuning framework that integrates Conformal Prediction (CP)-based losses with CE loss to optimize accuracy alongside confidence that better aligns with widely used top-K metrics. CPFT embeds CP principles into the training loop using differentiable proxy losses and computationally efficient calibration strategies, enabling the generation of high-confidence prediction sets. These sets focus on items with high relevance while maintaining robust coverage guarantees. Extensive experiments on five real-world datasets and four distinct sequential models demonstrate that CPFT improves precision metrics and confidence calibration. Our results highlight the importance of confidence-aware fine-tuning in delivering accurate, trustworthy recommendations that enhance user satisfaction.