Hyunmin Lee

163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea

Hello, my name is Hyunmin Lee and I am currently working on my Master’s degree in the Department of Computer Science and Engineering, in the University of Seoul. I’m under the supervision of Prof. Hyunggu Jung at Human-Centered Artificial Intelligence Lab (HCAIL).

My fields of interest are human-computer interaction(HCI), artificial intelligence(AI), accessibility, and data visualization. Feel free to contact me via e-mail for any questions or any kind of collaboration or cooperation.

selected publications

2024

IJCAI

SaGol: Using MiniGPT-4 to Generate Alt Text for Improving Image Accessibility

Yunseo Moon, Hyunmin Lee, SeungYoung Oh, and 1 more author

In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, Aug 2024

Demo Track
Data in Brief

Creating a bias-free dataset with food delivery app reviews under data poisoning attack

Hyunmin Lee, SeungYoung Oh, JinHyun Han, and 1 more author

Data in Brief, Aug 2024

Abs

In online food delivery apps, customers write reviews to reflect their experiences. However, certain restaurants use a “review event” strategy to solicit favorable reviews from customers and boost their revenue. Review event is a marketing strategy where a restaurant owner gives free services to customers in return for a promise to write a review. Nevertheless, current datasets of app reviews for food delivery services neglect this situation. Furthermore, there appears to be an absence of datasets with reviews written in Korean. To solve this gap, this paper presents a dataset that contains reviews obtained from restaurants on a Korean app which use a review event strategy. A total of 128,668 reviews were gathered from 136 restaurants through crawling reviews using the Selenium library in Python. The dataset consists of detailed information of each review which contains information about ordered dishes, each review’s written time, whether the food image is included in the review or not, and various star ratings such as total, taste, quantity, and delivery ratings. This dataset supports an innovative process of preparing AI training data for achieving fairness AI by proposing a bias-free dataset of food delivery app reviews with data poisoning attacks as an example. Additionally, the dataset is beneficial for researchers who are examining review events or analyzing the sentiment of food delivery app reviews.