OpenRaiser/Intern-Atlas huggingface dataset:
EFT-LR: Benchmarking Learning Rate Policies in Parameter-Efficient Large Language Model Fine-tuning
Authors: Md. Tasnim Jawad, Yanzhao Wu
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have achieved extensive impacts across various real-world data mining applications. Given the extremely high cost of training or fine-tuning LLMs, parameter-efficient fine-tuning (e.g., LoRA) has emerged as a popular and practical approach for adapting pre-trained general-purpose LLMs to specific downstream tasks. Among the various hyperparameters involved in parameter-efficient fine-tuning of LLMs, the learning rate (LR) plays a crucial role in determining the overall performance. However, it lacks a systematic benchmark framework to explore and understand how different LR policies influence the effectiveness of parameter-efficient LLM fine-tuning, which makes it challenging to select an optimal LR policy. To address this critical research gap, this paper introduces a systematic benchmark, EFT-LR, for assessing and selecting LR policies for effective parameter-efficient fine-tuning of LLMs. We first present a collection of seven popular LR policies spanning three major categories in the literature. We then perform parameter-efficient fine-tuning of LLMs using these LR policies and assess fine-tuned LLMs on eight downstream tasks. Our empirical analysis using EFT-LR provides an in-depth investigation of the impacts of different LR policies on parameter-efficient LLM fine-tuning, offering practical guidelines for practitioners. We provide the source code at https://github.com/mlsysx/EFT-LR.
The Yelp Collaborative Knowledge Graph
Authors: Theis E. Jendal, Mads Corfixen, Magnus Olesen, Peter Dolog, Katja Hose, Daniele Dell&apos, Aglio, Matteo Lissandrini
Venue: CIKM
Year: 2025

Yelp Open Dataset (YOD) is a widely used dataset for Recommender Systems (RS). Multiple Knowledge Graphs (KGs) have been built for YOD, but they have various issues: the conversion processes usually do not follow state-of-the-art methodologies, fail to properly link to other KGs, do not link to existing vocabularies, ignore important data, and are generally of small size. Instead, we present the Yelp Collaborative Knowledge Graph (YCKG), where we correctly integrating taxonomies, product categories, business locations, and the Yelp social network, through common practices within the semantic web community, overcoming all these issues. As a result, the YCKG includes 150k businesses and 16.9M reviews from 1.9M distinct real users, resulting in over 244 million triples, 144 distinct predicates, for about 72 million resources, with an average in-degree and out-degree of 3.3 and 12.2, respectively. Further, we release both the data and the code used to generate the KG for inspection and further extensions. This dataset can be used to develop and test both recommendation and data-mining algorithms able to exploit rich and semantically meaningful knowledge. We publicize the code for the CKG construction on: https://github.com/MadsCorfixen/The-Yelp-Collaborative-Knowledge-Graph.
Leveraging Large Language Models for Complementary Product Ads Recommendation
Authors: Byung Eun Jeon, Ryan Bae, Xiao Bai
Venue: CIKM
Year: 2025

Recommending complementary products1 that fulfill a joint need (e.g., phone case for smartphone) are often overlooked by dynamic product advertising (DPA) systems despite their success on e-Commerce websites such as Amazon. Existing works on complementary product recommendation focus on mining frequently co-purchased products but suffer from low accuracy as co-purchased products are not always complements to each other. More recent works rely on human annotators to clean co-purchased product pairs and use to train end-to-end models for complementary product recommendation. However, unlike e-commerce websites, DPA systems usually do not have access to users' complete shopping history, making the identification of co-purchased products challenging. Moreover, depending on the product types, identifying the complements of a given product may require extensive domain knowledge that is not present in a pair of complementary products. In this work, we propose a novel generate-and-retrieval paradigm to make complementary product recommendations and explore the use of LLMs for this task. Specifically, we rely on LLMs to generate queries that describe the complements of an original product. The generated queries are then used to retrieve relevant products from a product index. The retrieved products are expected to be complementary to the original product. We design experiments using the public Amazon ESCI datasets and compare in-context learning with parameter efficient fine-tuning using models from the GPT and Gemini families for complementary product generation. Our evaluation shows that by leveraging the extensive knowledge of LLMs on product relationship, using only a small number of human-annotated examples, pre-trained LLMs with proper prompt outperform LLMs fine-tuned with tens of thousands human-annotated examples.
Local Large Language Models for Recommendation
Authors: Yujin Jeon, Jooyoung Kim, Joonseok Lee
Venue: CIKM
Year: 2025

Unlike traditional classification tasks, recommendation is inherently subjective-whether an item should be suggested depends not only on user preferences and item semantics, but also on latent behavioral patterns and contextual cues. While recent LLM-based recommenders excel at modeling semantics and intent through generative reasoning, they often fail to capture collaborative signals and suffer from inefficiencies when applied globally across large interaction spaces. We propose Local Large Language Models for Recommendation(L3Rec), a novel model-agnostic framework that integrates collaborative filtering(CF) with generative LLMs through localized modeling. Our approach first applies a light-weight CF model to derive user and item embeddings, then clusters them into behaviorally coherent subgroups. Each cluster is assigned a dedicated generative LLM-referred to as a local LLM-trained only on its corresponding data subset. This enables fine-grained personalization while improving training efficiency through parallelism. At inference time, predictions from local models are aggregated via a fusion strategy, with a global CF fallback when needed. To the best of our knowledge, this is the first LLM-based recommendation framework to incorporate local collaborative structure. Experiments show that it achieves state-of-the-art performance with significantly better scalability and efficiency.
Identifying Critical Segments Affecting Piano Performance Evaluation
Authors: Hyerim Jeon, Wen-Syan Li
Venue: CIKM
Year: 2025

Effective evaluation of expressive music performance should not only assess technical and interpretive quality but also support efficient practice by identifying musically critical segments. However, traditional expert feedback is often limited by accessibility and scalability. To address this, we investigate how musically critical segments can be automatically identified and interpreted to support personalized assessment of piano performance. We propose a framework that identifies expressive deviations in piano performance by comparing extracted feature values with reference guidelines derived from music sheet analysis and expert annotations. Critical segments are detected via SHAP-based feature importance and change-point analysis, while interpretable annotation is generated using a large language model, conditioned on feature descriptions and quantified deviations. We evaluate our framework on the PercePiano dataset and newly collected annotations, showing consistent improvements in predicted overall scores of the performance after applying the generated annotation. Annotations generated using a finetuned feature extraction model improved predicted scores by up to 8.76%, with greater alignment with expert labels in both segment coverage and overlap. SHAP-based analysis confirms that the model identifies musically important features, enhancing both interpretability of annotation and its relevance to musical evaluation. Our results demonstrate that the proposed framework produces interpretable, musically meaningful annotations aligned with expert evaluations, and can serve as a foundation for scalable, AI-assisted music education and assessment. Our code is available at: https://github.com/Hyerim-Jeon/critical_segments.
ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting
Authors: Hyotaek Jeon, Hyunwook Lee, Juwon Kim, Sungahn Ko
Venue: CIKM
Year: 2025

Traffic forecasting represents a crucial problem within intelligent transportation systems. In recent research, Large Language Models (LLMs) have emerged as a promising method, but their intrinsic design, tailored primarily for sequential token processing, introduces notable challenges in effectively capturing spatial dependencies. Specifically, the inherent limitations of LLMs in modeling spatial relationships and their architectural incompatibility with graph-structured spatial data remain largely unaddressed. To overcome these limitations, we introduce ST-LINK, a novel framework that enhances the capability of Large Language Models to capture spatio-temporal dependencies. Its key components are Spatially-Enhanced Attention (SE-Attention) and the Memory Retrieval Feed-Forward Network (MRFFN). SE-Attention extends rotary position embeddings to integrate spatial correlations as direct rotational transformations within the attention mechanism. This approach maximizes spatial learning while preserving the LLM's inherent sequential processing structure. Meanwhile, MRFFN dynamically retrieves and utilizes key historical patterns to capture complex temporal dependencies and improve the stability of long-term forecasting. Comprehensive experiments on benchmark datasets demonstrate that ST-LINK surpasses conventional deep learning and LLM approaches, and effectively captures both regular traffic patterns and abrupt changes.
Entity-Aware Generative Retrieval for Personalized Contexts
Authors: Jihyeong Jeon, Jiwon Lee, Cheol Ryu, U Kang
Venue: CIKM
Year: 2025

Given a user query containing ambiguous and user-specific references, how can we effectively retrieve personalized information? Personalized information retrieval (PIR) requires resolving context-dependent cues --- such as nicknames, personal locations, or temporal expressions. This poses challenges for conventional retrievers, including dense and generative models, which often struggle with entity ambiguity and generalization to user-specific contexts. In this paper, we propose PEARL (Personalized Entity-Aware Generative RetrievaL), a novel generative retrieval framework for personalized IR. PEARL addresses key challenges through three components: (i) entity-aware annotation with span-level regularization to reduce lexical sensitivity, (ii) prefix-based contrastive learning to capture structural alignment between lexically divergent query-passage pairs, and (iii) context diversification to improve robustness against user-specific variations. Empirical results on both an existing PIR dataset and our new large-scale synthetic benchmark PAIR show that PEARL consistently outperforms strong baselines under zero-shot evaluation. Notably, PEARL achieves the state-of-the-art performance in Hits@1 and MRR@10, demonstrating its effectiveness for retrieval in personalized user contexts. Our dataset is available at https://www.github.com/pearl-pair/pearl.
Seeing Through the Blur: Unlocking Defocus Maps for Deepfake Detection
Authors: Minsun Jeon, Simon S. Woo
Venue: CIKM
Year: 2025

The rapid advancement of generative AI has enabled the mass production of photorealistic synthetic images, blurring the boundary between authentic and fabricated visual content. This challenge is particularly evident in deepfake scenarios involving facial manipulation, but also extends to broader AI-generated content (AIGC) cases involving fully synthesized scenes. As such content becomes increasingly difficult to distinguish from reality, the integrity of visual media is under threat. To address this issue, we propose a physically interpretable deepfake detection framework and demonstrate that defocus blur can serve as an effective forensic signal. Defocus blur is a depth-dependent optical phenomenon that naturally occurs in camera-captured images due to lens focus and scene geometry. In contrast, synthetic images often lack realistic depth-of-field (DoF) characteristics. To capture these discrepancies, we construct a defocus blur map and use it as a discriminative feature for detecting manipulated content. Unlike RGB textures or frequency-domain signals, defocus blur arises universally from optical imaging principles and encodes physical scene structure. This makes it a robust and generalizable forensic cue. Our approach is supported by three in-depth feature analyses, and experimental results confirm that defocus blur provides a reliable and interpretable cue for identifying synthetic images. We aim for our defocus-based detection pipeline and interpretability tools to contribute meaningfully to ongoing research in media forensics. The implementation is publicly available at: https://github.com/irissun9602/Defocus-Deepfake-Detection
Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults
Authors: Donghyun Jeon, Lijing Zhu, Haifang Li, Pengze Li, Jingna Feng, Tiehang Duan, Houbing Herbert Song, Cui Tao, Shuteng Niu
Venue: CIKM
Year: 2025

Temporal Graph Neural Networks (TGNNs) have become indispensable for analyzing dynamic graphs in critical applications such as social networks, communication systems, and financial networks. However, the robustness of TGNNs against adversarial attacks, particularly sophisticated attacks that exploit the temporal dimension, remains a significant challenge. Existing attack methods for Spatio-Temporal Dynamic Graphs (STDGs) often rely on simplistic, easily detectable perturbations (e.g., random edge additions/deletions) and fail to strategically target the most influential nodes and edges for maximum impact. We introduce the High Impact Attack (HIA), a novel restricted black-box attack framework specifically designed to overcome these limitations and expose critical vulnerabilities in TGNNs. HIA leverages a data-driven surrogate model to identify structurally important nodes (central to network connectivity) and dynamically important nodes (critical for the graph's temporal evolution). It then employs a hybrid perturbation strategy, combining strategic edge injection (to create misleading connections) and targeted edge deletion (to disrupt essential pathways), maximizing TGNN performance degradation. Importantly, HIA minimizes the number of perturbations to enhance stealth, making it more challenging to detect. Comprehensive experiments on five real-world datasets and four representative TGNN architectures (TGN, JODIE, DySAT, and TGAT) demonstrate that HIA significantly reduces TGNN accuracy on the link prediction task, achieving up to a 35.55% decrease in Mean Reciprocal Rank (MRR) - a substantial improvement over state-of-the-art baselines. These results highlight fundamental vulnerabilities in current STDG models and underscore the urgent need for robust defenses that account for both structural and temporal dynamics. Code and Data are available at https://github.com/ryandhjeon/hia.
Frequency-Conditioned Diffusion Models for Time Series Generation
Authors: Seungwoo Jeong, Junghyo Sohn, Jaehyun Jeon, Heung-Il Suk
Venue: CIKM
Year: 2025

Time series data, widely used in fields such as climate studies, finance, and healthcare, often face scarcity in rare scenarios and privacy concerns, prompting growing interest in time series synthesis. Diffusion models have shown strong potential for generating high-quality data, but challenges remain in capturing long-range dependencies and complex patterns. We propose a novel diffusion model that integrates time-domain information with rich frequency-domain features, accounting for differences in noise decay rates across frequencies. Instead of arbitrary frequency splits used in prior works, we partition components based on spectral density, model them separately within the denoising backbone, and fuse them with time-domain features. This enables effective capture of both global and local patterns, enhancing representation of high- and low-frequency information. Extensive experiments on multiple public datasets show promising performance, and analyses including long-term generation and ablation studies demonstrate the model's ability to learn and represent complex time series distributions.
Watch Your Step: A Fine-Grained Evaluation Framework for Multi-hop Knowledge Editing in Large Language Models
Authors: Geunyeong Jeong, Juoh Sun, Harksoo Kim
Venue: CIKM
Year: 2025

Knowledge editing allows for targeted updates of specific factual information in Large Language Models (LLMs). While existing methods can effectively update localized facts, they often struggle to coherently integrate these updates into the model's broader knowledge structure. Multi-hop knowledge editing addresses this issue by aiming that edited information is consistently reflected throughout the multi-hop reasoning process. However, current evaluation methods primarily assess the correctness of the final answer, which cannot guarantee that the edited knowledge has been correctly integrated into the reasoning process. To address these limitations, we propose a novel evaluation framework to systematically examine how edited knowledge is integrated within a multi-hop reasoning process. We introduce three types of entity-level errors: (i) Entity Persistence, where outdated entities remain; (ii) Entity Mismatch, where unrelated entities appear; and (iii) Entity Distortion, where entities are morphologically distorted, such as misspellings or truncations. Our analysis reveals that these errors frequently occur even when the final answer is correct. Moreover, when the final answer is incorrect, Entity Mismatch Errors are commonly observed, indicating unintended side effects of knowledge editing. The code is available at https://github.com/KUNLP/multihop-edit-eval.
Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge
Authors: Yuhe Ji, Yilun Liu, Feiyu Yao, Minggui He, Shimin Tao, Xiaofeng Zhao, Chang Su, Xinhua Yang, Weibin Meng, Yuming Xie, Boxing Chen, Shenglin Zhang, Yongqian Sun
Venue: CIKM
Year: 2025

Log analysis represents a critical sub-domain within AI applications that facilitates automatic approaches to fault and error management of large-scaled software systems, saving labors of traditional manual methods. While existing solutions using large language models (LLMs) show promise, they are limited by a significant domain gap between natural and log languages (the latter contains rich domain-specific tokens such as status codes, IP addresses, resource pathes), which restricts their effectiveness in real-world applications. However, directly adapting general-purpose LLMs to log analysis using raw logs may degrade their performance due to inconsistent token distribution. In this paper, we present a domain adaptation approach that addresses these limitations by integrating interpretable domain knowledge into open-source LLMs through continual pre-training (CPT), which bridges this domain gap by adapting LLMs on interpretable natural texts with log knowledge (instead of raw logs) to reduce distribution discrepancy. To achieve this, we developed NLPLog, a comprehensive dataset containing over 250,000 question-answer pairs on log-related knowledge. Our resulting model, SuperLog, achieves the best performance across four log analysis tasks, with an average accuracy improvement of 12.01% over the second-best model. Ablation study also suggests advantages of domain adaption using interpretable log knowledge over using raw logs.
From Anchors to Answers: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models
Authors: Yanbiao Ji, Chang Liu, Xin Chen, Dan Luo, Mei Li, Yue Ding, Wenqing Lin, Hongtao Lu
Venue: CIKM
Year: 2025

Enabling large language models (LLMs) to effectively process and reason with graph-structured data remains a significant challenge despite their remarkable success in natural language tasks. Current approaches either convert graph structures into verbose textual descriptions, consuming substantial computational resources, or employ complex graph neural networks as tokenizers, which introduce significant training overhead. To bridge this gap, we present NT-LLM, a novel framework with an anchor-based positional encoding scheme for graph representation. Our approach strategically selects reference nodes as anchors and encodes each node's position relative to these anchors, capturing essential topological information without the computational burden of existing methods. Notably, we identify and address a fundamental issue: the inherent misalignment between discrete hop-based distances in graphs and continuous distances in embedding spaces. By implementing a rank-preserving objective for positional encoding pretraining, NT-LLM achieves superior performance across diverse graph tasks ranging from basic structural analysis to complex reasoning scenarios. Our comprehensive evaluation demonstrates that this lightweight yet powerful approach effectively enhances LLMs' ability to understand and reason with graph-structured information, offering an efficient solution for graph-based applications of language models.
SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
Authors: Pengyue Jia, Zhaocheng Du, Yichao Wang, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Qidong Liu, Huifeng Guo, Ruiming Tang
Venue: CIKM
Year: 2025

Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Conventional approaches typically employ surrogate models-such as decision trees or neural networks-to estimate feature importance. However, their effectiveness is inherently constrained, as these models may struggle under suboptimal training conditions, including feature collinearity, high-dimensional sparsity, and insufficient data. In this paper, we propose SELF, a SurrogatE-Light Feature selection method for deep recommender systems. SELF integrates semantic reasoning from Large Language Models (LLMs) with task-specific learning from surrogate models, enabling an automated and lightweight feature selection process. Specifically, LLMs first produce a semantically informed ranking of feature importance, which is subsequently refined by a surrogate model, effectively integrating general world knowledge with task-specific learning. Comprehensive experiments on three public datasets from real-world recommender platforms validate the effectiveness of SELF. To facilitate reproducibility, our code is publicly available.