Accurate and prompt answering framework based on customer reviews and question-answer pairs

Eun Kim, Hyejung Yoon, Jungeun Lee, Misuk Kim

Research output: Contribution to journalArticlepeer-review


As e-commerce markets have gradually expanded, online shopping malls have provided various services aiming to secure competitiveness. A service for providing an accurate and prompt response when a customer writes an inquiry regarding a product represents a space directly connected to the customer and plays an important role, as it is directly related to product sales. However, the current online shopping mall answering service has disadvantages, e.g., it takes time for an administrator to write an answer directly, or to provide an answer within a set of answers. In this paper, we propose an answer framework for solving this problem, based on customer reviews. When a user writes a query, the framework provides an appropriate answer in real time through the system's question-and-answer pairs and customer reviews. The framework's performance is verified through a qualitative evaluation. In addition, it is confirmed that a customized model for reflecting the characteristics of each shopping mall can be created by using additional information from the collected data. The proposed framework is expected to support customers’ online shopping through more reliable and efficient information retrieval, and to reduce shopping mall operation and maintenance costs.

Original languageEnglish
Article number117405
JournalExpert Systems with Applications
StatePublished - 1 Oct 2022


  • Customer reviews
  • E-commerce market
  • Natural language processing
  • Question answering


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