Natural Language Explainable Recommendation with Robustness Enhancement

Natural language explainable recommendation has become a promising direction to facilitate more efficient and informed user decisions. Previous models mostly focus on how to enhance the explanation accuracy. However, the robustness problem has been largely ignored, which requires the explanations generated for similar user-item pairs should not be too much different. Different from traditional classification problems, improving the robustness of natural languages has two unique characteristics: (1) Different token importances, that is, different tokens play various roles in representing the complete sentence, and the robustness requirements for predicting them should also be different. (2) Continuous token semantics, that is, the similarity of the output should be judged based on semantics, and the sequences without any token-level overlap may also be highly similar. Based on these characteristics, we formulate and solve a novel problem in the recommendation domain, that is, robust natural language explainable recommendation. To the best of our knowledge, it is the first time in this field. Specifically, we base our modeling on adversarial robust optimization and design four types of heuristic methods to modify the adversarial outputs with weighted token probabilities and synonym replacements. Furthermore, to consider the mutual influence between the above characteristics, we regard language generation as a decision-making problem and design a dual-policy reinforcement learning framework to improve the robustness of the generated languages. We conduct extensive experiments to demonstrate the effectiveness of our framework.