OpenRaiser/Intern-Atlas huggingface dataset:
An Embarrassingly Simple but Effective Knowledge-enhanced Recommender
Authors: Haibo Ye, Lijun Zhang, Yuan Yao, Xinjie Li
Venue: CIKM
Year: 2025

Knowledge graphs (KG) have demonstrated significant potential in recommender systems by providing complementary semantic information that is typically absent in user-item interaction graphs (IG). While contrastive learning has emerged as a powerful paradigm for integrating these dual information sources, we identify a critical limitation in existing approaches: current methods fail to effectively balance the contrastive views derived from IG and KG, often resulting in performance degradation compared to using IG alone. To address this fundamental challenge, we propose SimKGCL, a novel contrastive learning framework that introduces a simple yet principled solution -- cross-view, layer-wise fusion between IG and KG representations prior to contrastive learning. This design ensures effective knowledge transfer while maintaining the discriminative power of contrastive objectives. Comprehensive experiments across three real-world benchmarks demonstrate that our approach not only consistently outperforms existing methods but also achieves remarkable efficiency gains. Our code is available through this link: https://figshare.com/articles/conference_contribution/SimKGCL/22783382.
AR2: Adversarial Reinforcement Learning for Abstract Reasoning in Large Language Models
Authors: Cheng-Kai Yeh, Hsing-Wang Lee, Chung-Hung Kuo, Hen-Hsen Huang
Venue: CIKM
Year: 2025

Abstraction--the ability to recognize and distill essential computational patterns from complex problem statements--is a foundational skill in computer science, critical both for human problem-solvers and coding-oriented large language models (LLMs). Despite recent advances in training LLMs for code generation using reinforcement learning (RL), most existing approaches focus primarily on superficial pattern recognition, overlooking explicit training for abstraction. In this study, we propose AR2 (Adversarial Reinforcement Learning for Abstract Reasoning), a novel framework explicitly designed to enhance the abstraction abilities of LLMs. AR2 employs a teacher model to transform kernel problems into narrative-rich, challenging descriptions without changing their fundamental logic. Simultaneously, a student coding model is trained to solve these complex narrative problems by extracting their underlying computational kernels. Experimental results demonstrate that AR2 substantially improves the student model's accuracy on previously unseen, challenging programming tasks, underscoring abstraction as a key skill for enhancing LLM generalization.
Multi-Armed Bandits with Biased and Heteroscedastic Auxiliary Rewards
Authors: Zechen Yin, Zhixuan Fang
Venue: CIKM
Year: 2025

We study the multi-armed bandits with auxiliary rewards problem, in which pulling an arm yields not only a primary reward but also a set of auxiliary rewards, which represents some low-quality data. The auxiliary reward distribution can be biased and have higher variances than the primary reward distribution. We analyze the regret lower bound with general-order cumulative volume function, and deduce the conditions under which an algorithm can outperform the classical optimal regret bound without auxiliary rewards, attained by the state-of-the-art (SOTA) algorithm Asymptotically-Optimal-UCB (AO-UCB). Then we propose the BVA-MIN-UCB algorithm, which carefully incorporates the primary and auxiliary rewards by adjusting the potential biases and different variances. We show that BVA-MIN-UCB always performs no worse than AO-UCB asymptotically, and nearly matches the regret lower bound, even up to a constant factor. Finally, we conduct numerical experiments to demonstrate the effectiveness of our algorithm.
Exploring Iterative Refinement for Nested Named Entity Recognition with IoU-aware Denoising Diffusion
Authors: Qiaoxuan Yin, Jianquan Ouyang, Huanrong Tang
Venue: CIKM
Year: 2025

Named entity recognition (NER) is a key task in natural language processing, but existing methods often fail to effectively handle nested structures due to fuzzy entity boundaries and structural ambiguity. To address this challenge, we propose a novel nested NER method based on an IoU-aware denoising diffusion model, which formulates the nested NER task as a generative denoising process that progressively recovers gold entity spans from noisy span proposals. We generate noisy samples during training by gradually adding Gaussian noise to the ground-truth entity boundaries. We then train a denoiser incorporating a top-k selective attention mechanism to refine entity span proposals iteratively. To strengthen the alignment between boundary localization and entity classification, we introduce an IoU-aware loss function that optimizes the overlap between predicted and ground-truth spans. This design more accurately guides boundary regression and effectively reduces misalignment caused by conventional regression losses. Our model leverages sentence features and timesteps as conditional inputs to capture contextual information throughout the denoising process. During inference, the model generates final entity predictions by starting from random noise spans and iteratively refining them through a multi-step reverse diffusion process. We conduct extensive experiments on four nested NER datasets, ACE2004, ACE2005, GENIA, and KBP2017, as well as two flat NER datasets, CoNLL2003 and OntoNotes. Experimental results show that the proposed method consistently outperforms existing advanced models across all benchmarks, demonstrating its effectiveness.
Uncertain Boundaries: A Tutorial on Copyright Challenges and Cross-Disciplinary Solutions for Generative AI
Authors: Zhipeng Yin, Zichong Wang, Avash Palikhe, Wenbin Zhang
Venue: CIKM
Year: 2025

As generative artificial intelligence (AI) becomes increasingly prevalent in creative industries, intellectual property issues have come to the forefront, especially regarding AI-generated content that closely resembles human-created works. Recent high-profile incidents involving AI-generated outputs reproducing copyrighted materials underscore the urgent need to reassess current copyright frameworks and establish effective safeguards against infringement. To this end, this tutorial provides a structured overview of copyright challenges in generative AI across the entire development lifecycle. It begins by outlining key copyright principles relevant to generative models, then explores methods for detecting and evaluating potential infringement in generated outputs. The session also introduces strategies to safeguard creative content and training data from unauthorized replication, including mitigation techniques during model training. Finally, it reviews existing regulatory frameworks, highlights unresolved research questions, and offers recommendations to guide future work in this evolving area.
DPT: Dynamic Preference Transfer for Cross-Domain Sequential Recommendation
Authors: Xiang Ying, Rui Ding, Yue Zhao, Mei Yu, Mankun Zhao
Venue: CIKM
Year: 2025

Cross-domain sequential recommendation aims to generate accurate recommendations by leveraging users' historical interactions across domains. However, existing methods have two limitations: 1) When transferring user's preferences from the source domain, they encode preferences into a static and holistic representation, ignoring the rich information inherent in the dynamic evolution of user preferences over time; 2) They adopt a distribution-agnostic full-transfer strategy, failing to effectively limit the transfer degree of source-domain preferences according to different data distributions, which poses a risk of negative transfer. To address these issues, we propose the Dynamic Preference Transfer (DPT) model. Unlike existing methods, DPT places greater emphasis on the dynamic transfer of real-time preferences. First, DPT captures the causal features through the causal self-attention mechanism, and then realizes dynamic preference transfer at each time step via the causal cross-attention mechanism, thereby tracking the temporal dynamics of preferences from source domains. Second, to mitigate the negative transfer issue, a temperature-controlled mechanism is designed to adaptively balance source and target domain preferences, leveraging a temperature-controlled sigmoid function to effectively suppress interference from irrelevant preferences. Experimental results on multiple benchmark datasets show that the proposed method achieves significant performance improvements compared with the state-of-the-art (SOTA) methods, verifying its effectiveness and superiority. The codes are available in https://github.com/iryand/DPT.
Continual Recommender Systems
Authors: Hyunsik Yoo, Seongku Kang, Hanghang Tong
Venue: CIKM
Year: 2025

Modern recommender systems operate in uniquely dynamic settings: user interests, item pools, and popularity trends shift continuously, and models must adapt in real time without forgetting past preferences. While existing tutorials on continual or lifelong learning cover broad machine learning domains (e.g., vision and graphs), they do not address recommendation-specific demands-such as balancing stability and plasticity per user, handling cold-start items, and optimizing recommendation metrics under streaming feedback. This tutorial aims to make a timely contribution by filling that gap. We begin by reviewing the background and problem settings, followed by a comprehensive overview of existing approaches. We then highlight recent efforts to apply continual learning to practical deployment environments, such as resource-constrained systems and sequential interaction settings. Finally, we discuss open challenges and future research directions. We expect this tutorial to benefit researchers and practitioners in recommender systems, data mining, AI, and information retrieval across academia and industry.
FAIR-SE: Framework for Analyzing Information Disparities in Search Engines with Diverse LLM-Generated Personas
Authors: Jaebeom You, Seung-Kyu Hong, Ling Liu, Kisung Lee, Hyuk-Yoon Kwon
Venue: CIKM
Year: 2025

Search engine personalization, while enhancing user satisfaction, can lead to information disparities. Previous studies on this topic face limitations, such as the absence of context-aware data collection, superficial URL-level analysis, and human-dependent annotations. We propose FAIR-SE, a Framework for Analyzing Information dispaRities in Search Engines that addresses these challenges through AWS Lambda-based concurrent data collection and LLM-generated persona-based content analysis. We collected search results across four user contexts (Search History, Geo-location, Language Preference, and Access Environment) and analyzed them through four analytical perspectives (Political Leaning, Topic-specific Stance, Subjectivity, and Bias). Experiments conducted on two globally prominent search engines across nine controversial topics demonstrate the efficacy of FAIR-SE regarding benchmark accuracy, persona consistency, and ability to reflect real-world discourse patterns across diverse topics. Our statistical analysis identifies distinct search engine characteristics and demonstrates significant information disparities in our case studies examining regional disparities in search results. Our code and datasets are publicly available at: https://github.com/bigbases/FAIR-SE.
MOVER: Multimodal Optimal Transport with Volume-based Embedding Regularization
Authors: Haochen You, Baojing Liu
Venue: CIKM
Year: 2025

Recent advances in multimodal learning have largely relied on pairwise contrastive objectives to align different modalities, such as text, video, and audio, in a shared embedding space. While effective in bi-modal setups, these approaches struggle to generalize across multiple modalities and often lack semantic structure in high-dimensional spaces. In this paper, we propose MOVER, a novel framework that combines optimal transport-based soft alignment with volume-based geometric regularization to build semantically aligned and structured multimodal representations. By integrating a transport-guided matching mechanism with a geometric volume minimization objective (GAVE), MOVER encourages consistent alignment across all modalities in a modality-agnostic manner. Experiments on text-video-audio retrieval tasks demonstrate that MOVER significantly outperforms prior state-of-the-art methods in both zero-shot and finetuned settings. Additional analysis shows improved generalization to unseen modality combinations and stronger structural consistency in the learned embedding space.
LLM-based Interactive Coding Education via Predictive Query Management and Student-Centered Fine-Tuning: Design and Implementation with 1500-Student Class Data
Authors: Geonjae Youn, Jonghoon Lee, Joongheon Kim, Chuck Yoo
Venue: CIKM
Year: 2025

Large-scale university courses face significant challenges. Teaching assistants are overwhelmed by the large number of student questions, limiting their ability to provide detailed and individualized support. As a result, students-especially those who are struggling-receive less tailored assistance, further widening gaps in academic performance. To address these challenges, we propose student-centered AI learning assistant (SCALA), a large language model (LLM)-based interactive tutoring system that incorporates student needs and learning expectations. SCALA consists of two main components, i.e., predictive query management and student-centered fine tuning. The first component anticipates common student questions via LLM agent debate. Each agent interacts using a combination of lecture content and student interactions in chat logs, collaboratively predicting what the students will likely ask. This fosters learning in students by generating and presenting relevant queries that guide their learning. On the other hand, the second component is fine-tuned on a 14k-question Python-tutoring dataset, curated based on in-depth student interviews to reflect real learning expectations. Our real-world experiments with 1500-student large-scale Python classes demonstrate that SCALA delivers more helpful and accurate responses compared to closed-form models (e.g., GPT-4o), while significantly reducing latency.
Temporal Blocks with Memory Replay for Dynamic Graph Representation Learning
Authors: Zhigang Yu, Hao Yan, Ruochen Liu, Xianghan Wang, Haijun Zhang, Senzhang Wang
Venue: CIKM
Year: 2025

Dynamic graph representation learning (DGRL) aims to model the temporal evolution of graph structure and attributes, thereby generating low-dimensional node representations at different time steps. Most prevailing snapshot-based methods construct snapshots independently in time, assigning each interaction to a single snapshot. However, such a design limits the ability to capture long-range temporal patterns, leading to the forgetting of prior interactions and reducing the capacity of the model to recognize causal dependencies across events. To address this issue, we construct temporal blocks with the memory replay mechanism by sequentially merging several adjacent snapshots to capture long-range temporal patterns and causal dependencies over time. Building on this, we propose a novel dynamic graph representation learning model named TBD. Specifically, the model first encodes each temporal block using a graph neural network (GNN), and then captures cross-block dynamics through a Multi-Feature Gated Recurrent Unit (MF-GRU) that incorporates structural embeddings and a feature-aware gating mechanism to adapt to evolving graph structures. Furthermore, we introduce a Structure-Aware Node Smoothness Constraint (SA-NSC) to enforce temporal consistency while retaining adaptability to structural changes. Extensive experiments on multiple real-world datasets demonstrate that TBD consistently achieves superior performance, validating its effectiveness and robustness.
CPSRank: Unsupervised Keyphrase Extraction via Contextual Perturbation
Authors: Hyunwook Yu, Minju Kim, Euijin Kim, Mucheol Kim
Venue: CIKM
Year: 2025

The importance of a phrase within a document becomes most evident through its absence rather than its presence. Inspired by this observation, we redefine keyphrases as those whose removal most disrupts the document's meaning. Traditional unsupervised methods typically rely on document-level signals, such as term frequency or phrase-to document similarity, which overlook the contextual contribution of a phrase. This paper proposes CPSRank, an unsupervised keyphrase extraction method that evaluates the semantic importance of candidate phrases via a contextual perturbation score (CPS). The CPS quantifies the critical role of each phrase by combining contextual perturbation and content loss. CPSRank outperforms existing baselines in terms of F1 scores while providing deeper insights into the semantic value of keyphrases. We release our code at https://github.com/Splo2t/CPSRank.
StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning
Authors: Yuanqing Yu, Zhefan Wang, Weizhi Ma, Shuai Wang, Chuhan Wu, Zhiqiang Guo, Min Zhang
Venue: CIKM
Year: 2025

Despite their powerful text generation capabilities, large language models (LLMs) still struggle to effectively utilize external tools to solve complex tasks, a challenge known as tool learning. Existing methods primarily rely on supervised fine-tuning, treating tool learning as a text generation problem while overlooking the decision-making complexities inherent in multi-step contexts. In this work, we propose modeling tool learning as a dynamic decision-making process and introduce StepTool, a novel step-grained reinforcement learning framework that enhances LLMs' capabilities in multi-step tool use. StepTool comprises two key components: Step-grained Reward Shaping, which assigns rewards to each tool interaction based on its invocation success and contribution to task completion; and Step-grained Optimization, which applies policy gradient methods to optimize the model across multiple decision steps. Extensive experiments across diverse benchmarks show that StepTool consistently outperforms both SFT-based and RL-based baselines in terms of task Pass Rate and Recall of relevant tools. Furthermore, our analysis suggests that StepTool helps models discover new tool-use strategies rather than merely re-weighting prior knowledge. These results highlight the importance of fine-grained decision modeling in tool learning and establish StepTool as a general and robust solution for enhancing multi-step tool use in LLMs. Code and data are available at https://github.com/yuyq18/StepTool.
TriSeRec: A Tri-view Representation Learning Framework for Sequential/Session-based Recommendation
Authors: Xinchen Yuan, Yichao Lu
Venue: CIKM
Year: 2025

Sequential/session-based recommendation models aim to learn evolving user preferences from historical user behaviors. State-of-the-art sequential/session-based recommendation models often use graph neural networks or self-attention as their building blocks. Graph neural networks excel at learning local patterns encoded in graph-structured data and have therefore shown great performance on session-based recommendation datasets, where user interactions are usually relatively short. Self-attentive models, on the other hand, are much more powerful in capturing long-range dependencies and are able to outperform graph neural network-based approaches on sequential recommendation, where longer user interactions are more frequent. As such, the recommender systems community has noted a lack of a unified framework that can simultaneously achieve great performance on both sequential and session-based recommendation. In an effort to fill this gap, this paper presents TriSeRec, a Tri-view representation learning framework for Sequential/session-based Recommendation. By converting interaction sequences into two graphical views and one sequential view, three view-specific user representations are learned by TriSeRec using graph neural networks and self-attention. The tri-view representation learning module, which is built upon the recently proposed generalized Cauchy-Schwarz divergence, disentangles and then fuses consistent and complementary information in all three views to form the final user representations for next-item predictions. Experiments on popular large-scale, real-world benchmark datasets show that TriSeRec achieves state-of-the-art performance on both sequential recommendation and session-based recommendation.
SEF-UQR: Scalable and Efficient Privacy-Preserving Federated Updating QR Factorization
Authors: Haonan Yuan, Wenyuan Wu, Jingwei Chen
Venue: CIKM
Year: 2025

Applications in real-time machine learning and data analysis often require incremental updates to matrix decompositions as new data arrive. This capability is particularly crucial for streaming PCA, online learning, and iterative optimization algorithms, where data are continuously generated from distributed sources. However, privacy constraints prevent direct data sharing among participants, making collaborative QR decomposition updates challenging. To address this, we present SEF-UQR, a scalable and efficient framework for federated QR updates that focuses on incremental row-addition updates-common in streaming-data scenarios-while leveraging homomorphic encryption and interactive ciphertext protocols to protect both inputs and intermediate computations. SEF-UQR achieves accuracy on par with insecure recomputation, maintaining a mean squared error (MSE) below 1e-12. Empirical results demonstrate that SEF-UQR delivers at least a 10× runtime improvement over existing state-of-the-art methods employing fully homomorphic encryption, confirming its effectiveness for privacy-sensitive, real-time federated data analysis.
The 1st Workshop on LLM Agents for Social Simulation
Authors: Yige Yuan, Junkai Zhou, Bingbing Xu, Liang Pang, Du Su, An Zhang, Teng Xiao, Fengli Xu, Zhaochun Ren, Xu Chen
Venue: CIKM
Year: 2025

Social simulation has long played a crucial role in exploring the mechanisms underlying human behavior and societal structures. Traditional social simulation relies on rule-based or statistical models, which makes it difficult to capture the complexity and variability of the real world. With the emergence and rapid development of large language model (LLM), new frontiers have been opened toward leveraging LLMs as agent to model human behavior and interactions. This cutting-edge direction has gained significant attention and demonstrated promising results, not only advancing research across a wide range of social science disciplines, but also enabling practical applications in role-playing scenarios. However, this field still faces multiple challenges, such as capturing real-world social phenomena, eliminating bias or ethical considerations, and ensuring usability and reliability. This workshop on LLM Agent for Social Simulation (LASS) aims to bring together researchers and practitioners from diverse backgrounds to foster interdisciplinary collaboration, address key challenges, explore new technologies, and chart promising future directions in this rapidly evolving field.