  In Sequential Recommendation Systems, Cross-Entropy (CE) loss is commonly
used but fails to harness item confidence scores during training. Recognizing
the critical role of confidence in aligning training objectives with evaluation
metrics, we propose CPFT, a versatile framework that enhances recommendation
confidence by integrating Conformal Prediction (CP)-based losses with CE loss
during fine-tuning. CPFT dynamically generates a set of items with a high
probability of containing the ground truth, enriching the training process by
incorporating validation data without compromising its role in model selection.
This innovative approach, coupled with CP-based losses, sharpens the focus on
refining recommendation sets, thereby elevating the confidence in potential
item predictions. By fine-tuning item confidence through CP-based losses, CPFT
significantly enhances model performance, leading to more precise and
trustworthy recommendations that increase user trust and satisfaction. Our
extensive evaluation across five diverse datasets and four distinct sequential
models confirms CPFT's substantial impact on improving recommendation quality
through strategic confidence optimization. Access to the framework's code will
be provided following the acceptance of the paper.
