Synthesizing Aspect-Driven Recommendation Explanations from Reviews
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 2427-2434. https://doi.org/10.24963/ijcai.2020/336
Explanations help users make sense of recommendations, increasing the likelihood of adoption. Existing approaches to explainable recommendations tend to rely on rigidly standardized templates, only allowing fill-in-the-blank aspect-level sentiments. For more flexible, literate, and varied explanations that cover various aspects of interest, we propose to synthesize an explanation by selecting snippets from reviews to optimize representativeness and coherence. To fit the target user's aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of varying product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text generation.
Machine Learning: Interpretability
Data Mining: Mining Text, Web, Social Media