User Review Writing via Interview with Dialogue Systems (2024)

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 readers’ 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.

## introduction user reviews posted on e-commerce and review sites benefit both buyers and sellers. for users considering a purchase, these reviews provide product details and insights, thereby supporting the decision-making process of purchase. additionally, they act as feedback for sellers, enabling product quality improvement. reviews that contain specific product details or are based on personal user experiences are especially valuable. however, writing these insightful reviews can be time-consuming and labor-intensive, as the task requires careful language and proofreading for potential buyers. to reduce users' burden of writing reviews, researchers (e.g., @xcite ) have focused on automated review generation, thereby making the review creation process more efficient. despite these efforts, the ability of existing review generation systems to personalize for users and products remains limited. previous studies have explored utilizing a wide range of information about products and users for generating reviews. however, available subjective information is limited. specifically, without direct input from the user, incorporating the user's actual experiences with the product into the generated review is challenging. this constraint significantly limits the system's ability to personalize for the user. to overcome this problem, we focused on supporting the creation of reviews by directly eliciting infor-mation about products from users. in this study, we propose the novel utilization of dialogue systems for creating user reviews. figure 1 shows an example of the review creation process supported by the dialogue system according to our proposed method. first, the dialogue system acts as an interviewer, eliciting user opinions on products through interview dialogues. second, the review text generator generates review text based on the dialogue history. finally, the rating predictor predicts a rating consistent with the generated review text. our method allows users to easily create reviews by simply interacting with the system, thus reducing the effort involved in review creation. to evaluate our method, we implemented a system incorporating our approach using gpt-4. subsequently, we conducted experiments using our system, collecting data on dialogues between the system and users, the generated reviews, predicted ratings, and participants' feedback on our system. we discuss the effectiveness of our method after analyzing the collected data. in summary, our main contributions are as follows: ## methodology to create useful reviews, reviewers must provide detailed product information. interview dialogue systems are employed to effectively elicit this information. to enhance readability, we propose organizing the dialogue history into a non-dialogue format. our method comprises three processes: interview dialogue, review text generation, and rating prediction. in this paper, the systems that perform these processes are referred to as the "interview dialogue system," the "review text generator," and the "rating predictor," respectively. our system utilizes these components in sequence to generate reviews as the output. an overview of our system is shown in figure 2 . we use the gpt-4-0613 model to implement our system. ## experiments we aim to facilitate the review-writing process for reviewers and provide helpful reviews to readers. to investigate the practicality of our method, we conducted evaluations from the perspectives of system users (section 4.1) and review readers (section 4.2). ## conclusion in this study, we present a novel method for utilizing dialogue systems to facilitate user review creation. our approach involves three processes: interview dialogue, review text generation, and rating prediction. although ensuring the fluency of the system-generated reviews remains a challenge, our method provides high-quality and helpful reviews for both reviewers and their readers. our method possesses high scalability. for instance, feeding product descriptions into our interview dialogue system could lead to deeper interview dialogues about more detailed information. however, our experiments have shown that even without such extensions, our system is capable of providing reviews that are more helpful than human-written ones. furthermore, adapting our dialogue system's strategies to user preferences during review writing could improve user experience. further research can accomplish this objective by conducting a more detailed analysis of user preferences. yicheng zou, bolin zhu, xingwu hu, tao gui, and qi zhang. 2021. low-resource dialogue summarization with domain-agnostic multi-source pretraining. in proceedings of the 2021 conference on empirical methods in natural language processing, pages 80-91, online and punta cana, dominican republic. association for computational linguistics.