Curr-RLCER:Curriculum Reinforcement Learning For Coherence Explainable Recommendation
This work addresses the issue of incoherence in explainable recommendation systems, which is incremental as it builds on existing methods by focusing on alignment between ratings and explanations.
The authors tackled the problem of incoherence between rating predictions and generated explanations in explainable recommendation systems by proposing Curr-RLCER, a reinforcement learning framework with curriculum learning and coherence-driven rewards, which showed effectiveness in experiments on three datasets.
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.