1. 

   Retrieval-Based Disease Prediction for Myocardial Injury after Noncardiac Surgery: Leveraging Language Models as Diagnostic Tools

   Namjun Park,  Dongguen Ko,  Dongjun Lee, and 2 more authors

   AAAI 2024 Spring Symposium on Clinical Foundation Models, 2024 (accepted)

2023

1. 

   How Important is Periodic Model Update in Recommender System?

   Hyunsung Lee, Sungwook Yoo, Dongjun Lee, and 1 more author

   In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

   Abs

   In real-world recommender model deployments, the models are typically retrained and deployed repeatedly. It is the rule-of-thumb to periodically retrain recommender models to capture up-to-date user behavior and item trends. However, the harm caused by delayed model updates has not been investigated extensively yet. in this perspective paper, we formulate the delayed model update problem and quantitatively demonstrate the delayed model update actually harms the model performance by increasing the number of cold users and cold items increase and decreasing overall model performances. These effects vary across different domains having different characteristics. Upon these findings, we further argue that although the delayed model update has negative effects on online recommender model deployment, yet it has not gathered enough attention from research communities. We argue our verification of the relationship between the model update cycle and model performance calls for further research such as faster model training, and more efficient data pipelines to keep the model more up-to-date with the latest user behaviors and item trends.

2. 

   AmpliBias: Mitigating Dataset Bias through Bias Amplification in Few-Shot Learning for Generative Models

   Donggeun Ko, Dongjun Lee, Namjun Park, and 3 more authors

   In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

   Abs

   Deep learning models exhibit a dependency on peripheral attributes of input data, such as shapes and colors, leading the models to become biased towards these certain attributes that result in subsequent degradation of performance. In this paper, we alleviate this problem by presenting sysname, a novel framework that tackles dataset bias by leveraging generative models to amplify bias and facilitate the learning of debiased representations of the classifier. Our method involves three major steps. We initially train a biased classifier, denoted as f_b, on a biased dataset and extract the top-K biased-conflict samples. Next, we train a generator solely on a bias-conflict dataset comprised of these top-K samples, aiming to learn the distribution of bias-conflict samples. Finally, we re-train the classifier on the newly constructed debiased dataset, which combines the original and amplified data. This allows the biased classifier to competently learn debiased representation. Extensive experiments validate that our proposed method effectively debiases the biased classifier.

3. 

   Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation

   Dongjun Lee, Donggeun Ko, and Jaekwang Kim

   arXiv preprint arXiv:2308.03400, 2023

   Abs

   Sequential recommendation addresses the issue of preference drift by predicting the next item based on the user’s previous behaviors. Recently, a promising approach using contrastive learning has emerged, demonstrating its effectiveness in recommending items under sparse user-item interactions. Significantly, the effectiveness of combinations of various augmentation methods has been demonstrated in different domains, particularly in computer vision. However, when it comes to augmentation within a contrastive learning framework in sequential recommendation, previous research has only focused on limited conditions and simple structures. Thus, it is still possible to extend existing approaches to boost the effects of augmentation methods by using progressed structures with the combinations of multiple augmentation methods. In this work, we propose a novel framework called Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation(HCLRec) to overcome the aforementioned limitation. Our framework leverages existing augmentation methods hierarchically to improve performance. By combining augmentation methods continuously, we generate low-level and high-level view pairs. We employ a Transformers-based model to encode the input sequence effectively. Furthermore, we introduce additional blocks consisting of Transformers and position-wise feed-forward network(PFFN) layers to learn the invariance of the original sequences from hierarchically augmented views. We pass the input sequence to subsequent layers based on the number of increment levels applied to the views to handle various augmentation levels. Within each layer, we compute contrastive loss between pairs of views at the same level. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art approaches and that HCLRec is robust even when faced with the problem of sparse interaction.

1. 

   Self-Interactive Attention Netwroks via Factorization Machinces for Click-Through Rate Prediction

   Dongjun Lee,  Hyunsung Lee, and Jaewang Kim

   In Proceedings of the 24nd International Symposium on Advanced Intelligent Systems, 2023

2. 

   Elevating CTR Prediction: Field Interaction, Global Context Integration, and High-Order Representations

   Sojeong Kim,  Dongjun Lee, and Jaewang Kim

   The 39th ACM/SIGAPP Symposium On Applied Computing, 2024

3. 

   Debiasing via Amplifying Bias with Latent Diffusion Model

   Dongguen Ko,  Dongjun Lee,  Namjun Park, and 2 more authors

   underreview