Publications

2026

1. Preprint

   Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification

   Junghoon Kim, Seungyoon Choi, Hyunsung Kim, and Chanyoung Park

   In Under review, 2026
2. Preprint

   Beyond Time Series: Integrating Categorical Context for Robust Time Series Classification

   Seungyoon Choi, Hyunchul Kim, Yerin Kim, Sangheon Lee, and Chanyoung Park

   In Under review, 2026
3. CIKM

   Compositional Spectral Prompts for LLM-based Online Time Series Forecasting

   Seungyoon Choi, Hyunchul Kim, Jae-Gil Lee, and Chanyoung Park

   In ACM International Conference on Information and Knowledge Management, 2026
4. CIKM

   DefaultGNN: A Dual-Perspective GNN Framework for Predicting Corporate Default from Buyer-Seller Transaction Networks

   Junghoon Kim, Hyunsung Kim, Seungyoon Choi, Kyoungyong Park, Jihun Lee, Yonggu Ji, and Chanyoung Park

   In ACM International Conference on Information and Knowledge Management (Applied Research Track), 2026

2025

1. SIGIR

   Dynamic Time-aware Continual User Representation Learning

   Seungyoon Choi, Sein Kim, Hongseok Kang, Wonjoong Kim, and Chanyoung Park

   In ACM SIGIR Conference on Research and Development in Information Retrieval, 2025

   Abs arXiv HTML Code

   Traditional user modeling (UM) approaches have primarily focused on designing models for a single specific task, but they face limitations in generalization and adaptability across various tasks. Recognizing these challenges, recent studies have shifted towards continual learning (CL)-based universal user representation learning aiming to develop a single model capable of handling multiple tasks. Despite advancements, existing methods are in fact evaluated under an unrealistic scenario that does not consider the passage of time as tasks progress, which overlooks newly emerged items that may change the item distribution of previous tasks. In this paper, we introduce a practical evaluation scenario on which CL-based universal user representation learning approaches should be evaluated, which takes into account the passage of time as tasks progress. Then, we propose a novel framework Dynamic Time-aware conTinual user representatiOn learner, named DITTO, designed to alleviate catastrophic forgetting despite continuous shifts in item distribution, while also allowing the knowledge acquired from previous tasks to adapt to the current shifted item distribution. Through our extensive experiments, we demonstrate the superiority of DITTO over state-of-the-art methods under a practical evaluation scenario.

2024

1. KDD

   Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System

   Sein Kim^*, Hongseok Kang^*, Seungyoon Choi, Donghyun Kim, Minchul Yang, and Chanyoung Park

   In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

   Abs arXiv HTML Code

   Collaborative filtering recommender systems (CF-RecSys) have shown successive results in enhancing the user experience on social media and e-commerce platforms. However, as CF-RecSys struggles under cold scenarios with sparse user-item interactions, recent strategies have focused on leveraging modality information of user/items (e.g., text or images) based on pre-trained modality encoders and Large Language Models (LLMs). Despite their effectiveness under cold scenarios, we observe that they underperform simple traditional collaborative filtering models under warm scenarios due to the lack of collaborative knowledge. In this work, we propose an efficient All-round LLM-based Recommender system, called A-LLMRec, that excels not only in the cold scenario but also in the warm scenario. Our main idea is to enable an LLM to directly leverage the collaborative knowledge contained in a pre-trained state-of-the-art CF-RecSys so that the emergent ability of the LLM as well as the high-quality user/item embeddings that are already trained by the state-of-the-art CF-RecSys can be jointly exploited. This approach yields two advantages: (1) model-agnostic, allowing for integration with various existing CF-RecSys, and (2) efficiency, eliminating the extensive fine-tuning typically required for LLM-based recommenders.

2. WWW

   DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning

   Seungyoon Choi^*, Wonjoong Kim^*, Sungwon Kim, Yeonjun In, Sein Kim, and Chanyoung Park

   In The Web Conference (Oral), 2024

   Abs arXiv HTML Code

   We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods. Existing rehearsal-based GCL methods select the most representative nodes for each class and store them in a replay buffer for later use in training subsequent tasks. However, we discovered that considering only the class representativeness of each replayed node makes the replayed nodes to be concentrated around the center of each class, incurring a potential risk of overfitting to nodes residing in those regions, which aggravates catastrophic forgetting. Moreover, as the rehearsal-based approach heavily relies on a few replayed nodes to retain knowledge obtained from previous tasks, involving the replayed nodes that have irrelevant neighbors in the model training may have a significant detrimental impact on model performance. In this paper, we propose a GCL model named DSLR, specifically, we devise a coverage-based diversity (CD) approach to consider both the class representativeness and the diversity within each class of the replayed nodes. Moreover, we adopt graph structure learning (GSL) to ensure that the replayed nodes are connected to truly informative neighbors. Extensive experimental results demonstrate the effectiveness and efficiency of DSLR.

2023

1. KDD

   Task-Equivariant Graph Few-shot Learning

   Sungwon Kim, Junseok Lee, Namkyeong Lee, Wonjoong Kim, Seungyoon Choi, and Chanyoung Park

   In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

   Abs arXiv HTML Code

   Although Graph Neural Networks (GNNs) have been successful in node classification tasks, their performance heavily relies on the availability of a sufficient number of labeled nodes per class. In real-world situations, not all classes have many labeled nodes and there may be instances where the model needs to classify new classes, making manual labeling difficult. To solve this problem, it is important for GNNs to be able to classify nodes with a limited number of labeled nodes, known as few-shot node classification. Previous episodic meta-learning based methods have demonstrated success in few-shot node classification, but our findings suggest that optimal performance can only be achieved with a substantial amount of diverse training meta-tasks. To address this challenge of meta-learning based few-shot learning (FSL), we propose a new approach, the Task-Equivariant Graph few-shot learning (TEG) framework. Our TEG framework enables the model to learn transferable task-adaptation strategies using a limited number of training meta-tasks, allowing it to acquire meta-knowledge for a wide range of meta-tasks. By incorporating equivariant neural networks, TEG can utilize their strong generalization abilities to learn highly adaptable task-specific strategies. As a result, TEG achieves state-of-the-art performance with limited training meta-tasks.