9 papers
Enriching Semantic Profiles into Knowledge Graph for Recommender Systems Using Large Language Models
Seokho Ahn, Sungbok Shin, Young-Duk Seo
Rich and informative profiling to capture user preferences is essential for improving recommendation quality. However, there is still no consensus on how best to construct and util…
Sensor Calibration Model Balancing Accuracy, Real-time, and Efficiency
Jinyong Yun, Hyungjin Kim, Seokho Ahn +2
Most on-device sensor calibration studies benchmark models only against three macroscopic requirements (i.e., accuracy, real-time, and resource efficiency), thereby hiding deployme…
Stochastic Deep Graph Clustering for Practical Group Formation
Junhyung Park, Hyungjin Kim, Seokho Ahn +1
While prior work on group recommender systems (GRSs) has primarily focused on improving recommendation accuracy, most approaches assume static or predefined groups, making them uns…
Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models
Hyungjin Kim, Seokho Ahn, Young-Duk Seo
Personalized generation in T2I diffusion models aims to naturally incorporate individual user preferences into the generation process with minimal user intervention. However, exist…
SenDaL: An Effective and Efficient Calibration Framework of Low-Cost Sensors for Daily Life
Seokho Ahn, Hyungjin Kim, Euijong Lee +1
The collection of accurate and noise-free data is a crucial part of Internet of Things (IoT)-controlled environments. However, the data collected from various sensors in daily life…
Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems
Minhye Jeon, Seokho Ahn, Young-Duk Seo
The use of knowledge graphs in recommender systems has become one of the common approaches to addressing data sparsity and cold start problems. Recent advances in large language mo…