UCEpic: Unifying Aspect Planning and Lexical Constraints for Explainable Recommendation

Personalized natural language generation for explainable recommendations plays a key role in justifying why a recommendation might match a user's interests. Existing models usually control the generation process by soft constraints (e.g.,~aspect planning). While promising, these methods struggle to generate specific information correctly, which prevents generated explanations from being informative and diverse. In this paper, we propose UCEpic, an explanation generation model that unifies aspect planning and lexical constraints for controllable personalized generation. Specifically, we first pre-train a non-personalized text generator by our proposed robust insertion process so that the model is able to generate sentences containing lexical constraints. Then, we demonstrate the method of incorporating aspect planning and personalized references into the insertion process to obtain personalized explanations. Compared to previous work controlled by soft constraints, UCEpic incorporates specific information from keyphrases and then largely improves the diversity and informativeness of generated explanations. Extensive experiments on RateBeer and Yelp show that UCEpic can generate high-quality and diverse explanations for recommendations.