OpenRaiser/Intern-Atlas huggingface dataset:
KV-Auditor: Auditing Local Differential Privacy for Correlated Key-Value Estimation
Authors: Jingnan Xu, Leixia Wang, Xiaofeng Meng
Venue: CIKM
Year: 2025

To protect privacy for data-collection-based services, local differential privacy (LDP) is widely adopted due to its rigorous theoretical bound on privacy loss. However, mistakes in complex theoretical analysis or subtle implementation errors may undermine its practical guarantee. To address this, auditing is crucial to confirm that LDP protocols truly protect user data. However, existing auditing methods, though, mainly target machine learning and federated learning tasks based on centralized differentially privacy (DP), with limited attention to LDP. Moreover, the few studies on LDP auditing focus solely on simple frequency estimation task for discrete data, leaving correlated key-value data - which requires both discrete frequency estimation for keys and continuous mean estimation for values - unexplored. To bridge this gap, we propose KV-Auditor, a framework for auditing LDP-based key-value estimation mechanisms by estimating their empirical privacy lower bounds. Rather than traditional LDP auditing methods that relies on binary output predictions, KV-Auditor estimates this lower bound by analyzing unbounded output distributions, supporting continuous data. Specifically, we classify state-of-the-art LDP key-value mechanisms into interactive and non-interactive types. For non-interactive mechanisms, we propose horizontal KV-Auditor for small domains with sufficient samples and vertical KV-Auditor for large domains with limited samples. For interactive mechanisms, we design a segmentation strategy to capture incremental privacy leakage across iterations. Finally, we perform extensive experiments to validate the effectiveness of our approach, offering insights for optimizing LDP-based key-value estimators.
LiveVal: Real-time and Trajectory-based Data Valuation via Adaptive Reference Points
Authors: Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia
Venue: CIKM
Year: 2025

Data valuation quantifies the contribution of each training data, enabling harmful data detection and enhancing model robustness. However, existing methods are typically post-hoc and require fully trained models, making them computationally expensive and unable to detect harmful data early in training. We propose LiveVal, a real-time and trajectory-based data valuation method that assesses training data by analyzing their influence on the optimization trajectory. LiveVal includes three key innovations: 1) a real-time valuation framework with minimal overhead, seamlessly integrated into standard training processes; 2) an adaptive reference point mechanism that assesses data impact on generalization; and 3) a normalization technique that ensures fair comparisons across training stages. Theoretical analysis shows that LiveVal achieves directional alignment, boundedness, stability, and fairness. Experiments demonstrate that LiveVal achieves up to 180× speedup over baseline methods while maintaining robust performance across diverse models and datasets.
Climber: Toward Efficient Scaling Laws for Large Recommendation Models
Authors: Songpei Xu, Shijia Wang, Da Guo, Xianwen Guo, Qiang Xiao, Bin Huang, Guanlin Wu, Chuanjiang Luo
Venue: CIKM
Year: 2025

Transformer-based generative models have achieved remarkable success across domains with various scaling law manifestations. However, our extensive experiments reveal persistent challenges when applying Transformer to recommendation systems: (1) Transformer scaling is not ideal with increased computational resources, due to structural incompatibilities with recommendation-specific features such as multi-source data heterogeneity; (2) critical online inference latency constraints (tens of milliseconds) that intensify with longer user behavior sequences and growing computational demands. We propose Climber, an efficient recommendation framework comprising two synergistic components: the model architecture for efficient scaling and the co-designed acceleration techniques. Our proposed model adopts two core innovations: (1) multi-scale sequence extraction that achieves a time complexity reduction by a constant factor, enabling more efficient scaling with sequence length; (2) dynamic temperature modulation adapting attention distributions to the multi-scenario and multi-behavior patterns. Complemented by acceleration techniques, Climber achieves a 5.15× throughput gain without performance degradation by adopting a ''single user, multiple item'' batched processing and memory-efficient Key-Value caching. Comprehensive offline experiments on multiple datasets validate that Climber exhibits a more ideal scaling curve. To our knowledge, this is the first publicly documented framework where controlled model scaling drives continuous online metric growth (12.19% overall lift) without prohibitive resource costs. Climber has been successfully deployed on Netease Cloud Music, one of China's largest music streaming platforms, serving tens of millions of users daily.
Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning
Authors: Yifei Xu, Jiaying Wu, Herun Wan, Yang Li, Zhen Hou, Min-Yen Kan
Venue: CIKM
Year: 2025

Hashtag trends ignite campaigns, shift public opinion, and steer millions of dollars in advertising spend, yet forecasting which tag goes viral is elusive. Classical regressors digest surface features but ignore context, while large language models (LLMs) excel at contextual reasoning but misestimate numbers. We present BuzzProphet, a reasoning-augmented hashtag popularity prediction framework that (1) instructs an LLM to articulate a hashtag's topical virality, audience reach, and timing advantage; (2) utilizes these popularity-oriented rationales to enrich the input features; and (3) regresses on these inputs. To facilitate evaluation, we release HashView, a 7,532-hashtag benchmark curated from social media. Across diverse regressor-LLM combinations, BuzzProphet reduces RMSE by up to 2.8% and boosts correlation by 30% over baselines, while producing human-readable rationales. Results demonstrate that using LLMs as context reasoners rather than numeric predictors injects domain insight into tabular models, yielding an interpretable and deployable solution for social media trend forecasting.
CLAP: Coreference-Linked Augmentation for Passage Retrieval
Authors: Huanwei Xu, Lin Xu, Liang Yuan
Venue: CIKM
Year: 2025

Large Language Model (LLM)-based passage expansion has shown promise for enhancing first-stage retrieval, but often underperforms with dense retrievers due to semantic drift and misalignment with their pretrained semantic space. Beyond this, only a portion of a passage is typically relevant to a query, while the rest introduces noise-an issue compounded by chunking techniques that break coreferential continuity. We propose Coreference-Linked Augmentation for Passage Retrieval (CLAP), a lightweight LLM-based expansion framework that segments passages into coherent chunks, resolves coreference chains, and generates localized pseudo-queries aligned with dense retriever representations. A simple fusion of global topical signals and fine-grained subtopic signals achieves robust performance across domains. CLAP yields consistent gains even as retriever strength increases, enabling first-stage retrieval to match or surpass second-stage rerankers such as BM25 + MonoT5-3B-exceeding the reranker by up to +20.68 nDCG@10 on ArguAna. These improvements are especially notable in out-of-domain settings, where conventional LLM-based expansion methods relying on domain knowledge often falter. CLAP instead adopts a logic-centric pipeline that enables robust, domain-agnostic generalization.
The Structure of Cross-National Collaboration in Open-Source Software Development
Authors: Henry George Xu, Tielin Katy Yu, Hao He, Hongbo Fang, Bogdan Vasilescu, Patrick S. Park
Venue: CIKM
Year: 2025

Open-source software (OSS) development platforms, such as GitHub, expand the potential for cross-national collaboration among developers by lowering the geographic, temporal, and coordination barriers that limited software innovation in the past. However, research has shown that the technological affordances that facilitate cross-national collaboration do not uniformly benefit all countries. Using the GitHub Innovation Graph dataset, which aggregates the complete cross-country collaborations among the entire population of GitHub developers, we present quantitative evidence of deep-seated religious and cultural affinities, shared colonial histories, and geopolitical factors structuring the collaborations between non-U.S. country pairs that become visible when the overarching dominance of the U.S. is removed from the data. This study highlights the opportunities to develop decentralizing strategies to facilitate new collaborations between developers in non-U.S. countries, thereby fostering the development of novel, innovative solutions. More generally, this study also underscores the importance of contextualizing user behavior and knowledge management in information systems with long-term, macro-social conditions in which these systems are inextricably embedded.
Contrastive Multi-View Graph Hashing
Authors: Yang Xu, Zuliang Yang, Kai Ming Ting
Venue: CIKM
Year: 2025

Multi-view graph data, which both captures node attributes and rich relational information from diverse sources, is becoming increasingly prevalent in various domains. The effective and efficient retrieval of such data is an important task. Although multi-view hashing techniques have offered a paradigm for fusing diverse information into compact binary codes, they typically assume attributes-based inputs per view. This makes them unsuitable for multi-view graph data, where effectively encoding and fusing complex topological information from multiple heterogeneous graph views to generate unified binary embeddings remains a significant challenge. In this work, we propose Contrastive Multi-view Graph Hashing (CMGHash), a novel end-to-end framework designed to learn unified and discriminative binary embeddings from multi-view graph data. CMGHash learns a consensus node representation space using a contrastive multi-view graph loss, which aims to pull k-nearest neighbors from all graphs closer while pushing away negative pairs, i.e., non-neighbor nodes. Moreover, we impose binarization constraints on this consensus space, enabling its conversion to a corresponding binary embedding space at minimal cost. Extensive experiments on several benchmark datasets demonstrate that CMGHash significantly outperforms existing approaches in terms of retrieval accuracy.
From Policy Comparison to Process Consistency and Beyond
Authors: Yifan Xu, Yujia Yin, Yiming Xing, Yifan Chen
Venue: CIKM
Year: 2025

Statistical Policy Comparison (SPC) assesses the equivalence of two stochastic policies (policy consistency) and has received broad attention. However, the SPC framework implicitly assumes the invariance of decision environments, and therefore fails to address a flurry of real-world data science applications. In this work, we refer to this overlooked issue as environment consistency, and together with policy consistency, this extends to a generalized concept process consistency for systematically comparing policy trials under the Markov decision process (MDP) framework. To address process consistency, we propose a unified comparison framework, extending beyond traditional statistical policy comparison studies by incorporating both policy and environment comparisons. For policy consistency, existing statistical policy comparison methods can be seamlessly integrated into our intentionally-designed framework without modification. Specifically for environment consistency (the focus of this work), we devise fine-grained return tests to capture shifts of key elements in MDPs; notably, under special cases where trajectory likelihood information is available or can be estimated, we introduce a trajectory test based on the likelihood ratio test (LRT), offering increased testing power. Extensive experiments demonstrate that our proposed testing methods achieve higher statistical power than existing approaches in testing process consistency, establishing their effectiveness across diverse real-world scenarios. Our code is available at https://github.com/bcxyf123/MDP-Testing.git.
Multi-Item-Query Attention for Stable Sequential Recommendation
Authors: Mingshi Xu, Haoren Zhu, Wilfred Siu Hung Ng
Venue: CIKM
Year: 2025

The inherent instability and noise in user interaction data challenge sequential recommendation systems. Prevailing masked attention models, relying on a single query from the most recent item, are sensitive to this noise, reducing prediction reliability. We propose the Multi-Item-Query attention mechanism (MIQ-Attn) to enhance model stability and accuracy. MIQ-Attn constructs multiple diverse query vectors from user interactions, effectively mitigating noise and improving consistency. It is designed for easy adoption as a drop-in replacement for existing single-query attention. Experiments show MIQ-Attn significantly improves performance on benchmark datasets.
Evaluating and Addressing Fairness Across User Groups in Negative Sampling for Recommender Systems
Authors: Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
Venue: CIKM
Year: 2025

Recommender systems trained on implicit feedback data rely on negative sampling to distinguish positive items from negative items for each user. Since the majority of positive interactions come from a small group of active users, negative samplers are often impacted by data imbalance, leading them to choose more informative negatives for prominent users while providing less useful ones for users who are not so active. This leads to inactive users being further marginalised in the training process, thus receiving inferior recommendations. In this paper, we conduct a comprehensive empirical study demonstrating that state-of-the-art negative sampling strategies provide more accurate recommendations for active users than for inactive users. We also find that increasing the number of negative samples for each positive item improves the average performance, but the benefit is distributed unequally across user groups, with active users experiencing performance gain while inactive users suffering performance degradation. To address this, we propose a group-specific negative sampling strategy that assigns smaller negative ratios to inactive user groups and larger ratios to active groups. Experiments on eight negative samplers show that our approach improves user-side fairness and performance when compared to a uniform global ratio.
JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference
Authors: Zongyue Xue, Siyuan Zheng, Shaochun Wang, Yiran Hu, Yuxin Yao, Shengran Wang, Haitao Li, Qingyao Ai, Yiqun Liu, Yun Liu, Weixing Shen
Venue: CIKM
Year: 2025

The integration of Large Language Models (LLMs) into legal practice raises pressing concerns about judicial fairness, particularly due to the nature of their ''black-box'' processes. This study introduces JustEva, a comprehensive, open-source evaluation toolkit designed to measure LLM fairness in legal tasks. JustEva features several advantages: (1) a structured label system covering 65 extra-legal factors; (2) three core fairness metrics -- inconsistency, bias, and imbalanced inaccuracy; (3) robust statistical inference methods; and (4) informative visualizations. The toolkit supports two types of experiments, enabling a complete evaluation workflow: (1) generating structured outputs from LLMs using a provided dataset, and (2) conducting statistical analysis and inference on LLMs' outputs through regression and other statistical methods. Empirical application of JustEva reveals significant fairness deficiencies in current LLMs, highlighting the lack of fair and trustworthy LLM legal tools. JustEva offers a convenient tool and methodological foundation for evaluating and improving algorithmic fairness in the legal domain.. The toolkit is available for deployment at https://github.com/KYSpring/ai_fairness_demo. A video demonstration of the toolkit is available at https://drive.google.com/file/d/1lB2U3q-kI5B5frv8iqVceVaA9Yks3kE6/view?usp=sharing.
FedSTEP: Asynchronous and Staleness-Aware Personalization for Efficient Federated Learning
Authors: Gang Yan, Jian Li, Wan Du
Venue: CIKM
Year: 2025

Personalized Federated Learning (PFL) aims to provide client-specific models that adapt to local data distributions while leveraging shared knowledge across clients. A common design in PFL is the head-representation architecture, which combines a shared global representation with a local head on each client. Although effective, deploying this architecture in real-world systems remains challenging due to the presence of stragglers and the high communication cost. To address these issues, we propose FedSTEP, a unified framework that integrates asynchronous training with dynamic communication sparsification. Specifically, it adaptively adjusts each client's local training duration and communication sparsity based on staleness, enabling more efficient coordination between local adaptation and global representation. This design mitigates the impact of stragglers and ensures robust performance in heterogeneous environments. We provide a theoretical analysis of the convergence behavior and communication efficiency of FedSTEP under standard assumptions. Extensive experiments on five public datasets demonstrate that FedSTEP consistently outperforms existing methods. It achieves up to 4.65% higher accuracy, a 3.68× speedup in training, and a 1.91× reduction in communication cost.
Compensating Information and Capturing Modal Preferences in Multimodal Recommendation: A Dual-Path Representation Learning Framework
Authors: Cairong Yan, Xubin Mao, Zijian Wang, Xicheng Zhao, Linlin Meng
Venue: CIKM
Year: 2025

In the context of information explosion, multimodal recommender systems (MMRS) have demonstrated great potential in capturing users' complex preferences and enhancing recommendation performance by integrating multimodal data such as images and text. However, multimodal data inherently suffers from semantic inconsistency, which can introduce information conflicts or noise. Moreover, users' reliance on different modalities varies dynamically with context and time (multimodal dynamic preferences). These challenges may lead to truth deviation and deep semantic mismatch, ultimately degrading recommendation performance. To address these issues, we propose Dual-Path Multimodal Recommendation (DPRec), a novel model that improves precision and robustness of the recommendation through cross-modal information compensation and dynamic modal preference learning. Specifically, DPRec first employs a cross-modal attention mechanism to dynamically model inter-modal correlations, effectively exploring complementary and shared features for robust user and item representations. Second, it integrates feature projection, modality alignment, and dynamic weighting mechanisms to adaptively adjust modality importance based on user context, ensuring flexibility in handling preference dynamics. Lastly, a modality contrastive loss is utilized to maximize mutual information between modalities, mitigating semantic mismatch by enhancing deep collaborative representations. Extensive experiments on three public datasets show that DPRec consistently outperforms state-of-the-art (SOTA) methods, achieving average improvements of 3.94% in Recall@20 and 3.84% in NDCG@20. Our code is publicly available at: https://anonymous.4open.science/r/DPRec-4D15.
STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
Authors: Zhenye Yang, Jinpeng Chen, Huan Li, Xiongnan Jin, Xuanyang Li, Junwei Zhang, Hongbo Gao, Kaimin Wei, Senzhang Wang
Venue: CIKM
Year: 2025

Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathers user preferences via a dialog module and builds user profiles through a recommendation module to generate appropriate recommendations. However, existing CRS faces challenges in capturing the deep semantics of user preferences and dialogue context. In particular, the efficient integration of external knowledge graph (KG) information into dialogue generation and recommendation remains a pressing issue. Traditional approaches typically combine KG information directly with dialogue content, which often struggles with complex semantic relationships, resulting in recommendations that may not align with user expectations. To address these challenges, we introduce STEP, a conversational recommender centered on pre-trained language models that combines curriculum-guided context-knowledge fusion with lightweight task-specific prompt tuning. At its heart, an F-Former progressively aligns the dialogue context with knowledge-graph entities through a three-stage curriculum, thus resolving fine-grained semantic mismatches. The fused representation is then injected into the frozen language model via two minimal yet adaptive prefix prompts: a conversation prefix that steers response generation toward user intent and a recommendation prefix that biases item ranking toward knowledge-consistent candidates. This dual-prompt scheme allows the model to share cross-task semantics while respecting the distinct objectives of dialogue and recommendation. Experimental results show that STEP outperforms mainstream methods in the precision of recommendation and dialogue quality in two public datasets. Our code is available: https://github.com/Alex-bupt/STEP.
Ordinal Embedding for Collaborative Filtering: A Unified Regularization for Enhanced Generalization and Interpretability
Authors: Jie Yang, Ling Luo, Nestor Cabello, Lars Kulik
Venue: CIKM
Year: 2025

Collaborative filtering is a primary paradigm of modern recommender systems. A typical practice is to embed collaborative signals into a latent space and infer recommendation scores based on the similarities between user and item embeddings. Besides inter-type similarities (i.e., user-item relationships), intra-type similarities (i.e., user-user, and item-item) are also essential as they capture the intrinsic structure of users and items. However, many existing recommendation models only learn inter-type similarities using objectives like ranking loss or binary classification loss, while neglecting intra-type similarities. Consequently, the intrinsic structures of users and items are often distorted in the latent space, where users with similar historical interactions diverge more than those dissimilar. In this study, we show the importance of preserving the ordinal relations of intra-type similarities. We provide a theoretical analysis suggesting that preserving intra-type similarity rankings can enhance a model's generalizability and interpretability. In addition, we propose a regularization that enforces a constraint on the rankings of intra-type similarities, ensuring that learning inter-type similarities does not break intrinsic ordinal structures. It can be seamlessly integrated into most latent factor models and can be jointly trained with their original objectives. Extensive experiments on 4 benchmark datasets and 5 representative models show that our ordinal regularization can consistently improve recommendation performance, and enhance the intra-type similarity coherence in the latent space. The results also exhibit enhanced generalizability and interpretability of recommendations.