Title: Explainable CTR Prediction via LLM Reasoning Show Chinese title

Title: 通过大语言模型推理的可解释点击率预测

Abstract: 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. Show Chinese abstract

Abstract: 推荐系统已成为现代用户体验的重要组成部分，但其决策过程缺乏透明度。 现有的可解释推荐方法受到事后范式的限制，其中解释生成器与底层推荐模型独立训练。 这种范式需要大量的人工数据构建工作，并引发了对解释可靠性的问题。 在本文中，我们提出了ExpCTR，一种新颖的框架，将基于大型语言模型的解释生成直接整合到点击率（CTR）预测过程中。 受强化学习最新进展的启发，我们采用了两种精心设计的奖励机制，LC对齐，确保解释反映用户意图，以及IC对齐，保持与传统基于ID的CTR模型的一致性。 我们的方法结合了高效的训练范式和LoRA以及三阶段迭代过程。 ExpCTR避免了对大量解释数据集的需求，同时促进了CTR预测和解释生成之间的协同作用。 实验结果表明，ExpCTR在三个真实世界数据集上显著提高了推荐准确性和可解释性。