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.

翻译：在序列推荐系统中，交叉熵损失函数虽被广泛使用，但未能充分利用训练过程中项目的置信度分数。鉴于置信度在协调训练目标与评估指标中的关键作用，我们提出CPFT框架——一种通过将基于共形预测的损失函数与交叉熵损失相结合进行微调的多功能框架，能够有效增强推荐置信度。CPFT通过动态生成高概率包含真实结果的项目集合，在不影响验证数据用于模型选择功能的前提下，将验证数据融入训练过程以丰富训练内容。这种创新方法与基于共形预测的损失函数相结合，显著强化了对推荐集合的优化能力，从而提升对潜在项目预测的置信度。通过基于共形预测的损失函数对项目置信度进行微调，CPFT大幅提升了模型性能，生成更精准可靠的推荐，进而增强用户信任与满意度。我们在五个不同数据集和四种不同序列模型上的广泛评估证实，CPFT通过策略性置信度优化显著提升了推荐质量。本框架的源代码将在论文接收后提供。