OpenRaiser/Intern-Atlas huggingface dataset:
OASIS: Open-world Adaptive Self-supervised and Imbalanced-aware System
Authors: Miru Kim, Mugon Joe, Minhae Kwon
Venue: CIKM
Year: 2025

The expansion of machine learning into dynamic environments presents challenges in handling open-world problems where label shift, covariate shift, and unknown classes emerge concurrently. Post-training methods have been explored to address these challenges, adapting models to newly emerging data. However, these methods struggle when the initial pre-training is performed on class-imbalanced datasets, limiting generalization to minority classes. To address this, we propose OASIS, an Open-world Adaptive Self-supervised and Imbalanced-aware System. OASIS consists of two learning phases: pre-training and post-training. The pre-training phase aims to improve the classification performance of samples near class boundaries via a novel borderline sample refinement step. Notably, the borderline sample refinement step critically improves the robustness of the decision boundary in the representation space. Through this robustness of the pre-trained model, OASIS generates reliable pseudo-labels, adapting the model against open-world problems in the post-training phase. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art post-training techniques in both accuracy and efficiency across diverse open-world scenarios.
When User Engagement Meets Structural Cohesiveness: A Decay-Driven Approach to Hypergraph Cores
Authors: Hyewon Kim, Minseok Kim, Dahee Kim, Junghoon Kim
Venue: CIKM
Year: 2025

Cohesive subgraph discovery in hypergraphs is essential for analysing complex group interactions in various domains such as e-commerce, social media, and collaboration networks. However, existing models are vulnerable to large hyperedges that artificially inflate connectivity, obscuring meaningful structure. We propose the (k,s)-core, a new model requiring each node to have at least k neighbours with a minimum interaction strength s, measured via a size-sensitive decay function. This penalises noisy co-occurrences while preserving strong local patterns. We develop an efficient algorithm with theoretical guarantees, and experiments on real-world datasets demonstrate improved compactness and robustness over prior methods.
A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation
Authors: Kyungho Kim, Sunwoo Kim, Geon Lee, Kijung Shin
Venue: CIKM
Year: 2025

In e-commerce, where users face a vast array of possible item choices, recommender systems are vital for helping them discover suitable items they might otherwise overlook. While many recommender systems primarily rely on a user's purchase history, recent multi-behavior recommender systems incorporate various auxiliary user behaviors, such as item clicks and cart additions, to enhance recommendations. Despite their overall performance gains, their effectiveness varies considerably between visited items (i.e., those a user has interacted with through auxiliary behaviors) and unvisited items (i.e., those with which the user has had no such interactions). Specifically, our analysis reveals that (1) existing multi-behavior recommender systems exhibit a significant gap in recommendation quality between the two item types (visited and unvisited items) and (2) achieving strong performance on both types with a single model architecture remains hallenging. To tackle these issues, we propose a novel multi-behavior recommender system, MEMBER. It employs a mixture-of-experts framework, with experts designed to recommend the two item types, respectively. Each expert is trained using a self-supervised method specialized for its design goal. In our comprehensive experiments, we show the effectiveness of MEMBER across both item types, achieving up to 65.46% performance gain over the best competitor in terms of Hit Ratio@20.
A Tutorial on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
Authors: Sunwoo Kim, Soo Yong Lee, Yue Gao, Alessia Antelmi, Mirko Polato, Kijung Shin
Venue: CIKM
Year: 2025

Higher-order interactions (HOIs) are ubiquitous in real-world networks, such as group discussions on online Q&A platforms, co-purchases of items in e-commerce, and collaborations of researchers. Investigation of deep learning for networks of HOIs, expressed as hypergraphs, has become an important agenda for the data mining and machine learning communities. As a result, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given this emerging trend, we provide a timely tutorial dedicated to HNNs. We cover the following topics: (1) inputs, (2) message passing schemes, (3) training strategies, (4) applications (e.g., recommender systems and time series analysis), and (5) open problems of HNNs. This tutorial is intended for researchers and practitioners who are interested in hypergraph representation learning and its applications.
Where Does Legal AI Fail? Evaluating RAG Pipelines
Authors: Yongjae Kim, Wonjae Lee
Venue: CIKM
Year: 2025

Retrieval Augmented Generation (RAG) frameworks have been integrated into LLMs for QA tasks, which have enabled lower levels of hallucination during the generation stage. Despite ample research in evaluating and detecting hallucination, research regarding its source in the legal domain is still minimal. In this paper, we break down each component of the RAG framework and analyze how they affect the quality of the generation. We use the expert answers of 1,039 real Korean civil complaints provided by the Korean Ministry of Economy and Finance to analyze the quality of generation across 12 experimental configurations that vary in retrieval, reranking, and generation components. To evaluate generation quality, we employ LLM-as-Judge comparing model outputs against expert responses across three dimensions: conclusion consistency, correctness and incorrectness. We validate our findings through logistic regression analysis, establishing statistical relationships between component metrics (Recall@200, NDCG@20) and generation consistency. Despite differing performance across models, two patterns emerge from the generation evaluation: increased retrieval performance correlates with decreased generation quality, and increased reranker performance correlates with higher generation quality. Our work questions the conventional wisdom that ''more context is better'' and provides a systematic attribution framework for finding the origin of hallucination in legal RAG systems.
CR-SGCN: Unsupervised Signed Community Detection via Conductance Regularization
Authors: Jeongseon Kim, Sungsu Lim
Venue: CIKM
Year: 2025

Community detection in signed networks is challenging due to the presence of both positive and negative edges, which violate the homophily assumption commonly used in traditional methods. In this paper, we present CR-SGCN, an unsupervised framework for community detection in signed networks. It combines a signed GCN encoder, a soft community assignment layer, and a degree-corrected stochastic block model decoder. To enhance boundary separation, we introduce an edge-level signed conductance regularization that pulls intra-community embeddings closer and pushes inter-community ones apart. Without requiring labels, CR-SGCN effectively captures community structure even under edge sparsity. Experiments on real-world signed networks show consistent gains in signed modularity and structural separation over existing baselines. The results demonstrate the robustness and effectiveness of CR-SGCN for unsupervised signed community detection.
From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets
Authors: Juwon Kim, Hyunwook Lee, Hyotaek Jeon, Seungmin Jin, Sungahn Ko
Venue: CIKM
Year: 2025

Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patterns were prevalent, but their structural vagueness and scale ambiguity hindered generalization. In contrast, deep learning models can effectively capture complex dynamics, yet often offer limited transparency. To bridge this gap, we propose a two-stage framework that integrates unsupervised pattern extracion with interpretable forecasting. (i) SIMPC segments and clusters multivariate time series, extracting recurrent patterns that are invariant to amplitude scaling and temporal distortion, even under varying window sizes. (ii) JISC-Net is a shapelet-based classifier that uses the initial part of extracted patterns as input and forecasts subsequent partial sequences for short-term directional movement. Experiments on Bitcoin and three S&P 500 equities demonstrate that our method ranks first or second in 11 out of 12 metric--dataset combinations, consistently outperforming baselines. Unlike conventional deep learning models that output buy-or-sell signals without interpretable justification, our approach enables transparent decision-making by revealing the underlying pattern structures that drive predictive outcomes.
RadialFocus: Geometric Graph Transformers via Distance-Modulated Attention
Authors: San Kim, Seungjun Lee, Sichan Oh, Jaekwang Kim
Venue: CIKM
Year: 2025

Graph Transformers (GTs) excel at long-range reasoning on graphs but often rely on costly positional encodings or auxiliary virtual nodes to perceive geometry. We present the RadialFocus Graph Transformer (RadialFocus), a geometry-aware GT that learns to modulate attention with a lightweight, distance-selective kernel. Each head is equipped with a differentiable radial basis function whose centre μ and width σ are trained end-to-end, boosting attention between nodes that lie inside its adaptive ''focus'' while gently suppressing others. Injecting the logarithm of this kernel into the pre-softmax logits preserves the stability and permutation invariance of standard self-attention, incurs negligible memory overhead, and removes the need for hand-crafted 3-D encodings or virtual nodes. On 3-D molecular benchmarks RadialFocus attains a validation MAE of 46.3, meV on PCQM4Mv2 with only 13 M parameters, surpassing models an order of magnitude larger. It also sets a new best average ROC-AUC (79.1 %) on MoleculeNet and reaches 0.957 MAE on PDBBind2020, a new high-water mark for binding-affinity prediction. The same architecture transfers to 2-D graphs, achieving 97.8 % accuracy on MNIST-Superpixel. Ablation studies indicate that the learned (μ, σ) capture task-relevant distance scales and that log-space fusion stabilises gradients. These findings suggest that a simple, learned distance modulation suffices to equip Transformers with strong geometric priors, enabling accurate and parameter-efficient reasoning across diverse graph domains.
From Menus to the Interactive Food-Ordering Systems
Authors: Min-Ji Kim, Seong-Jin Park, Jaehwan Ha, Ju-Won Seo, Dinara Aliyeva, Kang-Min Kim
Venue: CIKM
Year: 2025

Conversational interfaces have emerged as an accessible and user-friendly alternative to traditional touch-based self-service kiosks in food-ordering systems. Despite their promise, building such systems remains challenging due to the need for costly data annotation, store-specific model adaptation, and scalable deployment. In this study, we propose a fully automated, end-to-end framework that transforms structured menu databases into high-quality annotated datasets and efficiently deploys store-specific conversational models using a parameter-efficient fine-tuning method. Our approach fine-tunes only 0.9% of the backbone model parameters per store, enabling cost-effective and plug-and-play deployment across diverse environments. To enhance robustness, we further integrate a recommendation module that suggests alternative items when requested menu options are unavailable. Experimental results on data from 27 stores in South Korea demonstrate that our framework consistently outperforms existing data generation baselines in intent classification and slot filling performance, while maintaining high annotation quality. Simulated real-world voice-ordering scenarios confirm the practicality of our framework for rapid, scalable, and accessible deployment in real-world environments.
Smart ECU: Scalable On-Vehicle Deployment of Drivetrain Fault Classification Systems for Commercial Electric Vehicles
Authors: Jaeho Kim, Kwangryeol Park, Kyu Hwan Lee, Jeongmin Oh, Dongjin Park, Hyunseok Oh, Youngrock Chung, Kyung-Woo Lee, Dae-Un Sung, Seulki Lee
Venue: CIKM
Year: 2025

We present Smart ECU, the first on-vehicle drivetrain fault classification solution for motor-reducers on commercial electric vehicles (EVs), designed to be scalable in mass production. To develop and validate this system, we collect real-world vibration data from seven different EV models (e.g. Hyundai IONIQ 5, KIA EV6) and over 19 drivetrains under diverse driving conditions. This work addresses key challenges in deploying motor-reducer fault classification functionality onto an extremely resource-constrained ECU environment, facilitating on-vehicle deployment of PHM solutions on commercially manufactured EVs. Specifically, we tackle the following challenges: (1) real-vehicle data collection, (2) development under tight ECU resource constraints, (3) class imbalance between normal and fault conditions, and (4) scalability of the fault classification system across different EV models with limited fault data availability. We deploy and evaluate Smart ECU on both intra-car and inter-car scenarios, showing strong generalization performance in both setups. The proposed method enables rapid development of fault classification for new vehicle designs without requiring fault data from customer usage, significantly shortening the deployment timeline. Our solution addresses both technical and industrial challenges in deploying ECU-based smart diagnostics for commercial EVs, while also demonstrating broader applicability beyond drivetrain systems to other critical vehicle components. To the best of our knowledge, this is the first work to (1) collect real-world motor-reducer fault data, (2) implement a lightweight fault classification algorithm on ECUs, and (3) demonstrate its scalability across various EV types.
Exploring Diverse Sparse Network Structures via Dynamic Pruning with Weight Alignment
Authors: Jinwoo Kim, Jongyun Shin, Sangho An, Jangho Kim
Venue: CIKM
Year: 2025

Deep neural networks (DNNs) often require a large number of parameters, which has led to the development of model pruning techniques that remove weight connections. In this paper, we propose a new method to maximize the effect of finding sparse patterns through a gradient scaling technique that modifies the weight distribution. This approach allows for the exploration of more diverse sparse patterns compared to traditional dynamic pruning methods, leading to the discovery of stable subnetworks. Through various experiments, we demonstrate the importance of exploration in finding better sparse patterns. We achieve state-of-the-art performance across multiple network architectures on datasets such as CIFAR-10/100 and ImageNet. The code is available at https://github.com/Acasia/DWA.
RuSemCor: A Word Sense Disambiguation corpus for Russian
Authors: Alexander Kirillovich, Ilia Karpov, Natalia V. Loukachevitch, Maksim Kulaev, Dmitry Ilvovsky
Venue: CIKM
Year: 2025

We present RuSemCor, an open Word Sense Disambiguation (WSD) corpus for Russian. The corpus was constructed by manually linking tokens from the OpenCorpora corpus to senses in the Russian wordnet RuWordNet. It consists of 869 documents with 121,710 tokens of which 51,588 are wordnet annotated. The resource is represented using the NIF, OLiA, OntoLex, and Global WordNet ontologies and integrated into the Linguistic Linked Open Data cloud. We used RuSemCor as a diagnostic benchmark to evaluate a range of WSD methods. Our experiments yielded three main findings. 1)~Generative LLMs substantially outperform traditional knowledge-based methods such as Personalized PageRank. 2)~Despite their strengths, generative LLMs do not surpass encoder-based models specifically trained for WSD. 3)~Incorporating lexical-semantic relations from RuWordNet produces mixed results: it enhances the performance of encoder-based models and leading LLMs like GPT-4, DeepSeek, and Mistral 24B, but tends to degrade accuracy for smaller generative models such as GPT-3 and Mistral 7B. The resource is distributed under the CC BY-SA open license and is available at: https://github.com/LLOD-Ru/rusemcor.
Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
Authors: Yunyong Ko, Da Eun Lee, Song Kyung Yu, Sang-Wook Kim
Venue: CIKM
Year: 2025

Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-depth preliminary analysis, we observe two important characteristics of high-order dynamics in real-world networks: high-order relations tend to (O1) have a structural and temporal influence on other relations in a short term and (O2) periodically re-appear in a long term. In this paper, we propose LINCOLN, a method for Learning hIgh-order dyNamiCs Of reaL-world Networks, that employs (1) bi-interactional hyperedge encoding for short-term patterns, (2) periodic time injection and (3) intermediate node representation for long-term patterns. Via extensive experiments, we show that LINCOLN outperforms nine state-of-the-art methods in the dynamic hyperedge prediction task.
Exploring Database Normalization Effects on SQL Generation
Authors: Ryosuke Kohita
Venue: CIKM
Year: 2025

Schema design, particularly normalization, is a critical yet often overlooked factor in natural language to SQL (NL2SQL) systems. Most prior research evaluates models on fixed schemas, overlooking the influence of design on performance. We present the first systematic study of schema normalization's impact, evaluating eight leading large language models on synthetic and real-world datasets with varied normalization levels. We construct controlled synthetic datasets with formal normalization (1NF-3NF) and real academic paper datasets with practical schemes. Our results show that denormalized schemas offer high accuracy on simple retrieval queries, even with cost-effective models in zero-shot settings. In contrast, normalized schemas (2NF/3NF) introduce challenges such as errors in base table selection and join type prediction; however, these issues are substantially mitigated by providing few-shot examples. For aggregation queries, normalized schemas yielded better performance, mainly due to their robustness against the data duplication and NULL value issues that cause errors in denormalized schemas. These findings suggest that the optimal schema design for NL2SQL applications depends on the types of queries to be supported. Our study demonstrates the importance of considering schema design when developing NL2SQL interfaces and integrating adaptive schema selection for real-world scenarios.
Taming the Unicorn: Turning Generative AI Into a Workhorse How to Draw Boundaries, Handle Hallucinations, and Make AI Behave in Product
Authors: Marios Kokkodis, Purusoth Mahendran, Grace Boatwright
Venue: CIKM
Year: 2025

This work illustrates the process of turning speculative AI prototypes into reliable, production-grade systems. In particular, we discuss how we redesigned our search engine from keyword-based matching to a generative AI framework that interprets natural language queries and engages users through dynamic question answering, to provide a personalized experience. To build this framework, we created highly cross-functional processes, where content design, user experience research, engineering, and science worked iteratively in unison. In short, we do not chase unicorns; we put them to work safely and predictably.
Side Information Memory Network: Expanding the Breadth of User Behavior Sequences in Recommendation
Authors: Zhoufan Kong, Fan Zhang, Qijie Shen, Junyan Qiu
Venue: CIKM
Year: 2025

Research on sequence-based ranking models has been a popular field in recommendation systems. In recent years, numerous researchers have devoted themselves to expanding the content of user behavior sequences, such as longer sequences, more types of sequences, and more variable sequence periods. These studies have achieved promising results, especially in the direction of longer sequences, where a large number of industrial recommendation systems have demonstrated that longer sequences can lead to better performance. However, as the sequence length approaches the upper limit of user behavior occurrences, the marginal benefit of increasing sequence length is gradually diminishing. Against this backdrop, this paper proposes a sequence expansion framework based on Side Information Memory Network (SIMN). Based on SIMN, theoretically all item-side features can be incorporated into the sequence, while avoiding additional sample development costs and storage costs. Furthermore, considering the application of this framework in small to medium-sized recommendation systems, this paper proposes a Feature Auto Encoder-Decoder (FAED) module, which further reduces the storage cost of SIMN. This paper integrates SIMN and FAED into a unified multitask training framework for modeling and validates it on two industrial datasets. Experimental results demonstrate that SIMN-FAED can be integrated with most mainstream sequence modeling methods and achieve better performance, with broad application prospects.
Graph Neural Network Architecture Search via Hybrid Genetic Algorithm with Parallel Tempering
Authors: Maciej Krzywda
Venue: CIKM
Year: 2025

In recent years, there has been a surge of interest in harnessing graph neural networks (GNNs) for graph classification tasks. Despite the strong performance of manually designed GNN architectures, their development often relies on time-consuming, expert-driven trial and error, which may overlook promising design opportunities. To address this challenge, we propose a hybrid Genetic Algorithm and Parallel Tempering (GA+PT) framework for automated GNN architecture search. Our method systematically encodes a rich design space-including convolutional layer types (GCN, GraphSAGE, GAT, GIN, SGC), attention heads, hidden-layer widths, dropout rates, weight-initialization schemes, learning rates, and classifier structures-into evolutionary genotypes. A population-based GA explores this space via crossover and mutation, while an inner PT Markov Chain Monte Carlo procedure adaptively refines individual solutions under a temperature schedule to escape local optima. We validate our approach on benchmark graph datasets, demonstrating its ability to discover high-performing architectures that balance classification accuracy, macro-F1 score, and model complexity. The proposed GA+PT hybrid offers a robust, scalable, and resource-aware alternative to manual GNN design and purely gradient-based neural architecture search methods.