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This paper introduces Curr-RLCER, a reinforcement learning framework for explainable recommendation systems that addresses the incoherence between rating prediction and explanation generation. The framework uses curriculum learning to transition from basic predictions to open-ended explanation generation, with rewards designed to enhance system stability. A coherence-driven reward mechanism enforces alignment between generated explanations and predicted ratings, evaluated using a novel evaluation scheme.
Stop disjointedly predicting ratings and explanations in recommendation systems: Curr-RLCER uses reinforcement learning to ensure they actually make sense together.
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.
