Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products

TL;DR

This paper introduces a novel approach for predicting answers to product questions by leveraging answers from similar questions on similar products, especially useful when reviews are scarce, and demonstrates its effectiveness on large datasets.

Contribution

The work proposes a mixture-of-experts model that utilizes contextual similarity between products to improve answer prediction for new or unpopular products.

Findings

Model outperforms baselines on questions with many similar resolved questions.

Effective for products with limited review data.

Provides large-scale datasets for similar product questions and answers.

Abstract

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.