Review-Aware Answer Prediction for Product-Related Questions Incorporating Aspects
Refereed conference paper presented and published in conference proceedings


摘要In E-commerce sites, there are platforms for users to pose product-related questions and experienced customers may provide answers voluntarily. Among the questions asked by users, a large proportion of them are yes-no questions reflecting that users wish to know whether or not the product can satisfy a certain criterion or meet a certain expectation. Both Question Answering (QA) approaches and Community Question Answering methods are not suitable for answer prediction for new questions in this setting. The reasons are that questions are product-associated and many of them are concerned about user experiences and subjective opinions. In addition to existing question-answer pairs, user written reviews can provide useful clues for answer prediction. In this paper, we propose a new framework that can tackle the task of review-aware answer prediction for product-related questions. The aspect analytics model in this framework learns latent aspects as well as aspect-specific embeddings of reviews via a 3-order Autoencoder. One advantage of this learned model is that it can generate aspect-specific representations for new questions. The predictive answer model in our framework, learned jointly from existing questions, answers, and reviews, is able to predict the answers for new yes-no questions taking into consideration of aspects. Besides, our framework can provide supportive reviews grouped by relevant aspects serving as information for explainable answers. Experiment results on 15 different product categories from a large-scale benchmark E-commence QA dataset demonstrate the effectiveness of our framework.
著者Qian YU, Wai LAM
會議名稱11th ACM International Conference on Web Search and Data Mining, WSDM 2018
會議地點Los Angeles
會議論文集題名WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
出版社Association for Computing Machinery
頁次691 - 699

上次更新時間 2021-18-01 於 02:01