Explainable CTR Prediction via LLM Reasoning

TL;DR

ExpCTR is a novel framework integrating large language model-based explanations directly into CTR prediction, improving transparency and accuracy without extensive explanation datasets.

Contribution

It introduces a unified approach combining LLM explanations with CTR prediction using reinforcement learning and alignment rewards, reducing reliance on post-hoc methods.

Findings

Enhanced recommendation accuracy

Improved interpretability

Effective without large explanation datasets

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.