用大模型直接生成可解释的点击率预测，让推荐更透明可信。
Explainable CTR Prediction via LLM Reasoning
- 把大模型推理嵌入点击率预测流程，实时生成解释。
- 在三个真实数据集上同时提升预测准确率与解释一致性。
- 无需人工标注解释数据，适合需要透明推荐的场景。
推荐系统已成为现代用户体验的核心，但其决策过程缺乏透明度。现有可解释推荐方法受限于事后解释范式，即解释生成器独立于推荐模型训练，需大量人工构建数据，且解释可靠性存疑。本文提出ExpCTR框架，将基于大语言模型的解释生成直接融入点击率（CTR）预测过程。受强化学习启发，设计两种奖励机制：LC对齐确保解释反映用户意图，IC对齐保持与传统基于ID的CTR模型一致。采用高效训练策略（LoRA）与三阶段迭代流程，避免对大规模解释数据集的依赖，实现CTR预测与解释生成的协同优化。实验表明，ExpCTR在三个真实数据集上显著提升推荐精度与可解释性。
原文摘要 · Abstract (English)
Recommendation Systems have become integral to modern user experiences, but lack transparency in their decision-making processes. Existing explainable recommendation methods are hindered by reliance on a post-hoc paradigm, wherein explanation generators are trained independently of the underlying recommender models. This paradigm necessitates substantial human effort in data construction and raises concerns about explanation reliability. In this paper, we present ExpCTR, a novel framework that integrates large language model based explanation generation directly into the CTR prediction process. Inspired by recent advances in reinforcement learning, we employ two carefully designed reward mechanisms, LC alignment, which ensures explanations reflect user intentions, and IC alignment, which maintains consistency with traditional ID-based CTR models. Our approach incorporates an efficient training paradigm with LoRA and a three-stage iterative process. ExpCTR circumvents the need for extensive explanation datasets while fostering synergy between CTR prediction and explanation generation. Experimental results demonstrate that ExpCTR significantly enhances both recommendation accuracy and interpretability across three real-world datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。