User Review Writing via Interview with Dialogue Systems

TL;DR

This study proposes a novel use of dialogue systems to facilitate user review creation by generating reviews from information gathered during interview dialogues with users by generating reviews from information gathered during interview dialogues with users.

Abstract

User reviews on e-commerce and review sites are crucial for making purchase decisions, although creating detailed reviews is time-consuming and labor-intensive. In this study, we propose a novel use of dialogue systems to facilitate user review creation by generating reviews from information gathered during interview dialogues with users. To validate our approach, we implemented our system using GPT-4 and conducted comparative experiments from the perspectives of system users and review readers. The results indicate that participants who used our system rated their interactions positively. Additionally, reviews generated by our system required less editing to achieve user satisfaction compared to those by the baseline. We also evaluated the reviews from the reader' perspective and found that our system-generated reviews are more helpful than those written by humans. Despite challenges with the fluency of the generated reviews, our method offers a promising new approach to review writing.

Figures (5)

• Figure 1: Example of review creation supported by the proposed method. In the dialogue and review, the contents of the same-colored text correspond to each other.
• Figure 2: Overview of our system. First, the interview dialogue system interviews the user to elicit their impressions and requests about the product they used. Next, the review text generator uses the dialogue history as input to generate a review text. Finally, the rating predictor predicts a rating consistent with the sentiment of the generated review text.
• Figure 3: Participant responses to "In the past year, how often have you posted reviews?"
• Figure 4: Participant responses to questions on a Likert scale from 1 (Strongly disagree) to 5 (Strongly agree) in a post-interview survey. For each question, the upper bar shows the results from our system and the lower bar shows the baseline results.
• Figure 5: Participant responses to "If you had to edit and post a system-generated review to your satisfaction, how much of it would you need to rewrite?"