Curr-RLCER:Curriculum Reinforcement Learning For Coherence Explainable Recommendation

TL;DR

Curr-RLCER is a reinforcement learning framework that improves the coherence and transparency of explainable recommendation systems through curriculum learning and dynamic rating alignment.

Contribution

It introduces a novel curriculum learning approach with a coherence-driven reward mechanism for more coherent explanations in recommendation systems.

Findings

Effective in enhancing explanation coherence and recommendation stability.

Outperforms baseline methods on three datasets.

Code and datasets publicly available at GitHub.

Abstract

Explainable recommendation systems (RSs) are designed to explicitly uncover the rationale of each recommendation, thereby enhancing the transparency and credibility of RSs. Previous methods often jointly predicted ratings and generated explanations, but overlooked the incoherence of such two objectives. To address this issue, we propose Curr-RLCER, a reinforcement learning framework for explanation coherent recommendation with dynamic rating alignment. It employs curriculum learning, transitioning from basic predictions (i.e., click through rating-CTR, selection-based rating) to open-ended recommendation explanation generation. In particular, the rewards of each stage are designed for progressively enhancing the stability of RSs. Furthermore, a coherence-driven reward mechanism is also proposed to enforce the coherence between generated explanations and predicted ratings, supported by a specifically designed evaluation scheme. The extensive experimental results on three explainable recommendation datasets indicate that the proposed framework is effective. Codes and datasets are available at https://github.com/pxcstart/Curr-RLCER.