Jaekwang Kim

Sequential recommendation (SR) predicts a user's next action by modeling their historical interaction sequence, and it underpins many real-world platforms where accuracy directly impacts engagement and conversion. Recently, diffusion-based SR has gained traction by iteratively denoising corrupted representations, improving robustness and handling uncertainty when user intent is ambiguous. However, most diffusion-based SR methods still rely mainly on item IDs and learned latent embeddings, complemented by conditioning or regularization techniques. This ID-centric design limits semantic expressiveness: semantically similar items can remain distant under sparse feedback, while unrelated items may be confounded by co-occurrence and popularity biases. Prior work has attempted to inject semantics through contrastive learning or auxiliary objectives, but semantic signals are often incorporated indirectly and can weaken along the denoising trajectory, especially at inference when generation starts from noise. To address this limitation, we make semantics an explicit denoising target by introducing a semantic token whose target is the next-item semantic embedding in the same latent space as item embeddings, and we jointly denoise the item sequence and the semantic token. Building on this idea, we propose SEDRec (Semantic Entanglement generative Diffusion for sequential Recommendation), which entangles semantic and ID-based signals through history semantic conditioning, synchronized noise levels, semantic-to-target mixing, and intermediate alignment to a fixed semantic anchor. Across multiple benchmarks and evaluation metrics, extensive experiments demonstrate that SEDRec consistently outperforms strong baselines, and ablation studies confirm the contribution of each semantic entanglement component.

Consumer personas are essential for user understanding and marketing strategy, yet manual construction remains costly and difficult to scale. We propose a data-driven persona generation framework that systematically compares three interpretable text mining methods-SNA, LDA, and K-Means-across eleven experimental configurations and injects the resulting structured representations into LLM prompts. Experiments on two consumer review datasets (VOC: n=965; VAD: n=2,370) with N =20 repeated trials show that structured injection substantially outperforms the no-injection baseline (all p<0.001, Cohen's d=1.09-6.58 across matching and validity metrics). We further uncover a complexity paradox: K-Means alone consistently outperforms most pipeline configurations, and method selection-not combination strategy-is the primary determinant of persona generation quality, with combination effects varying across data construction strategies (synthetic Top-Down vs. real-world Bottom-Up).

Accurate prediction of postoperative complications, such as acute myocardial injury (AMI) and acute kidney injury (AKI), is essential for informed clinical decision-making and improved patient outcomes. However, conventional machine learning approaches often exhibit degraded performance in this setting due to severe class imbalance and the need for extensive feature preprocessing. To address these challenges, we propose a retrieval-based disease prediction (RBD) framework that leverages language models for postoperative risk assessment. The proposed framework converts heterogeneous preoperative and intraoperative clinical data into textual representations and retrieves relevant disease information by comparing patient-specific descriptions with predefined disease definitions of AMI and AKI. This retrieval-based formulation reduces the dependence on complex data normalization and resampling strategies commonly required by traditional models. Experimental results demonstrate that the RBD framework consistently outperforms existing machine learning methods in predicting postoperative complications under imbalanced data conditions. These findings indicate that retrieval-based language model analytics provide a promising approach for clinical decision support in postoperative care.

Network attacks have become a critical issue in the internet security domain. Artificial intelligence technology-based detection methodologies have attracted attention; however, recent studies have struggled to adapt to changing attack patterns and complex network environments. In addition, it is difficult to explain the detection results logically using artificial intelligence. We propose a method for classifying network attacks using graph models to explain the detection results. First, we reconstruct the network packet data into a graphical structure. We then use a graph model to predict network attacks using edge classification. To explain the prediction results, we observed numerical changes by randomly masking and calculating the importance of neighbors, allowing us to extract significant subgraphs. Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964, outperforming traditional machine learning and other graph models. The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results, thus explaining detection. In conclusion, this study demonstrates that graph-based models are suitable for network attack detection in complex environments, and the importance of graph neighbors can be calculated to efficiently analyze the results. This approach can contribute to real-world network security analyses and provide a new direction in the field.