Cross-Market Product-Related Question Answering

Abstract

Online shops such as Amazon, eBay, and Etsy continue to expand their presence in multiple countries, creating new resource-scarce marketplaces with thousands of items. We consider a marketplace to be resource-scarce when only limited user-generated data is available about the products (e.g., ratings, reviews, and product-related questions). In such a marketplace, an information retrieval system is less likely to help users find answers to their questions about the products. As a result, questions posted online may go unanswered for extended periods. This study investigates the impact of using available data in a resource-rich marketplace to answer new questions in a resource-scarce marketplace, a new problem we call cross-market question answering. To study this problem's potential impact, we collect and annotate a new dataset, XMarket-QA, from Amazon's UK (resource-scarce) and US (resource-rich) local marketplaces. We conduct a data analysis to understand the scope of the cross-market question-answering task. This analysis shows a temporal gap of almost one year between the first question answered in the UK marketplace and the US marketplace. Also, it shows that the first question about a product is posted in the UK marketplace only when 28 questions, on average, have already been answered about the same product in the US marketplace. Human annotations demonstrate that, on average, 65% of the questions in the UK marketplace can be answered within the US marketplace, supporting the concept of cross-market question answering. Inspired by these findings, we develop a new method, CMJim, which utilizes product similarities across marketplaces in the training phase for retrieving answers from the resource-rich marketplace that can be used to answer a question in the resource-scarce marketplace. Our evaluations show CMJim's significant improvement compared to competitive baselines.