  Providing natural language-based explanations to justify recommendations
helps to improve users' satisfaction and gain users' trust. However, as current
explanation generation methods are commonly trained with an objective to mimic
existing user reviews, the generated explanations are often not aligned with
the predicted ratings or some important features of the recommended items, and
thus, are suboptimal in helping users make informed decision on the
recommendation platform. To tackle this problem, we propose a flexible
model-agnostic method named MMI (Maximizing Mutual Information) framework to
enhance the alignment between the generated natural language explanations and
the predicted rating/important item features. Specifically, we propose to use
mutual information (MI) as a measure for the alignment and train a neural MI
estimator. Then, we treat a well-trained explanation generation model as the
backbone model and further fine-tune it through reinforcement learning with
guidance from the MI estimator, which rewards a generated explanation that is
more aligned with the predicted rating or a pre-defined feature of the
recommended item. Experiments on three datasets demonstrate that our MMI
framework can boost different backbone models, enabling them to outperform
existing baselines in terms of alignment with predicted ratings and item
features. Additionally, user studies verify that MI-enhanced explanations
indeed facilitate users' decisions and are favorable compared with other
baselines due to their better alignment properties.
