Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products
This addresses a bottleneck in e-commerce and recommendation systems for products with sparse data, though it is incremental as it builds on existing review-aware methods.
The paper tackles the problem of answering product-related questions for new or unpopular products lacking reviews by using answers from contextually similar products, and it shows that the model outperforms baselines for questions with roughly ten or more similar resolved questions.
Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention. Answering subjective and opinion-based questions is most challenging due to the dependency on customer-generated content. Previous works mostly focused on review-aware answer prediction; however, these approaches fail for new or unpopular products, having no (or only a few) reviews at hand. In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products. We measure the contextual similarity between products based on the answers they provide for the same question. A mixture-of-expert framework is used to predict the answer by aggregating the answers from contextually similar products. Empirical results demonstrate that our model outperforms strong baselines on some segments of questions, namely those that have roughly ten or more similar resolved questions in the corpus. We additionally publish two large-scale datasets used in this work, one is of similar product question pairs, and the second is of product question-answer pairs.