OpenRaiser/Intern-Atlas huggingface dataset:
EI-KGC: A Knowledge Graph Completion Model Based on Fine-Grained Element Interactions
Authors: Dong Li, Lingling Zhang, Yuhang Fan, Jingyou Sun, Xinyu Zhang, Baoyan Song
Venue: CIKM
Year: 2025

Most existing knowledge graph completion methods fail to model the fine-grained interactions among elements within triples, such as dependencies between entity attributes or contextual relationships involving predicates and entities. This limitation weakens their ability to infer implicit knowledge and hinders overall reasoning performance. To address this issue, we define a three-level classification of element interactions: Interactions between Elements at the Head Entity (IEH), Interactions between Elements at the Relationship (IER), and Interactions between Elements at the Tail Entity (IET), that systematically models the influence propagation patterns among knowledge graph triples at element level. Based on these interaction types, we propose a novel Knowledge Graph Completion Model Based on Fine-Grained Element Interactions (EI-KGC). Our model captures both global structural patterns and semantic dependencies within triples by combining GNN propagation with fine-grained interaction modeling. Experimental results show that the EI-KGC consistently outperforms traditional baseline models, demonstrating the high effectiveness of our proposed model.
SCAlign: Transaction Event Prediction via Multi-Scale Market Dynamics Alignment
Authors: Boyang Li, Lingzheng Zhang, Fugee Tsung, Xi Zhang
Venue: CIKM
Year: 2025

Event prediction plays a pivotal role in analyzing consumer behavior for inventory and pricing optimization. In dynamic financial markets, customer behavior is often influenced by price commitment policies, where the historical and pre-announced future transaction price dynamics can lead to complex behavior patterns, such as advance consumption or delayed purchasing. Therefore, these phenomena pose significant challenges to traditional event modeling approaches that rely solely on consumer behaviors. To address this problem, we propose SCAlign, a cross-domain and multi-scale framework for market dynamics alignment, designed for event prediction. Our model integrates both heterogeneous historical and limited observable future commitment prices at different scales, aligning customer behavior with market fluctuations across multiple time scales. Finally, through a Mixture-of-Experts (MoE) framework, the model dynamically fuses these aligned features, enabling adaptive selection of the relevant and appropriate representations for prediction tasks. Empirical evaluations across diverse transaction environments demonstrate that our model outperforms state-of-the-art prediction baselines. Furthermore, it achieves optimal performance across varying data scales, showcasing its robustness and generalizability.
UniECS: Unified Multimodal E-Commerce Search Framework with Gated Cross-modal Fusion
Authors: Zihan Liang, Yufei Ma, Zhipeng Qian, Huangyu Dai, Zihan Wang, Ben Chen, Chenyi Lei, Yuqing Ding, Han Li
Venue: CIKM
Year: 2025

The growth of e-commerce has created substantial demand for multimodal search systems that process diverse visual and textual inputs. Current e-commerce multimodal retrieval systems face two key limitations: they optimize for specific tasks with fixed modality pairings, and lack comprehensive benchmarks for evaluating unified retrieval approaches. To address these challenges, we introduce UniECS, a unified multimodal e-commerce search framework that handles all retrieval scenarios across image, text, and their combinations. Our work makes three key contributions. First, we propose a flexible architecture with a novel gated multimodal encoder that uses adaptive fusion mechanisms. This encoder integrates different modality representations while handling missing modalities. Second, we develop a comprehensive training strategy to optimize learning. It combines cross-modal alignment loss (CMAL), cohesive local alignment loss (CLAL), intra-modal contrastive loss (IMCL), and adaptive loss weighting. Third, we create M-BEER, a carefully curated multimodal benchmark containing 50K product pairs for e-commerce search evaluation. Extensive experiments demonstrate that UniECS consistently outperforms existing methods across four e-commerce benchmarks with fine-tuning or zero-shot evaluation. On our M-BEER bench, UniECS achieves substantial improvements in cross-modal tasks (up to 28% gain in R@10 for text-to-image retrieval) while maintaining parameter efficiency (0.2B parameters) compared to larger models like GME-Qwen2VL (2B) and MM-Embed (8B). Furthermore, we deploy UniECS in the e-commerce search platform of Kuaishou Inc. across two search scenarios, achieving notable improvements in Click-Through Rate (+2.74%) and Revenue (+8.33%). The comprehensive evaluation demonstrates the effectiveness of our approach in both experimental and real-world settings. Corresponding codes, models and datasets will be made publicly available at https://github.com/qzp2018/UniECS.
TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
Authors: Zida Liang, Changfa Wu, Dunxian Huang, Weiqiang Sun, Ziyang Wang, Yuliang Yan, Jian Wu, Yuning Jiang, Bo Zheng, Ke Chen, Silu Zhou, Yu Zhang
Venue: CIKM
Year: 2025

Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative models have demonstrated potential in enhancing recommendation systems; however, these models often exhibit limitations in optimizing retrieval tasks, primarily due to their reliance on autoregressive generation mechanisms. Conventional approaches introduce sequential dependencies that impede efficient retrieval, as they are inherently unsuitable for generating multiple items without positional constraints within a single request session. To address these limitations, we propose TBGRecall, a framework integrating Next Session Prediction (NSP), designed to enhance generative retrieval models for e-commerce applications. Our framework reformulation involves partitioning input samples into multi-session sequences, where each sequence comprises a session token followed by a set of item tokens, and then further incorporate multiple optimizations tailored to the generative task in retrieval scenarios. In terms of training methodology, our pipeline integrates limited historical data pre-training with stochastic partial incremental training, significantly improving training efficiency and emphasizing the superiority of data recency over sheer data volume. Our extensive experiments, conducted on public benchmarks alongside a large-scale industrial dataset from TaoBao, show TBGRecall outperforms the state-of-the-art recommendation methods, and exhibits a clear scaling law trend. Ultimately, NSP represents a significant advancement in the effectiveness of generative recommendation systems for e-commerce applications.
Calibrating on Kolmogorov-Arnold Network
Authors: Wenhao Liang, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
Venue: CIKM
Year: 2025

Kolmogorov-Arnold Networks (KANs) are neural architectures inspired by the Kolmogorov-Arnold representation theorem that leverage B-spline parameterizations for flexible, locally adaptive function approximation. Although KANs can capture complex nonlinearities beyond those modeled by standard Multi-Layer Perceptrons (MLPs), they frequently exhibit miscalibrated confidence estimates-manifesting as overconfidence in dense data regions and underconfidence in sparse areas. In this work, we systematically examine the impact of four critical hyperparameters -- Layer Width, Grid Order, Shortcut Function, and Grid Range -- on the calibration of KANs. Furthermore, we introduce a novel Temperature-Scaled Loss (TSL) that integrates a temperature parameter directly into the training objective, dynamically adjusting the predictive distribution during learning. Both theoretical analysis and extensive empirical evaluations on standard benchmarks demonstrate that TSL significantly reduces calibration errors, thereby improving the reliability of probabilistic predictions. Overall, our study provides actionable insights into the design of spline-based neural networks and establishes TSL as a robust, loss-agnostic solution for enhancing calibration.
Calibrating on Medical Segmentation Model through Signed Distance
Authors: Wenhao Liang, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
Venue: CIKM
Year: 2025

Classical overlap metrics such as Dice or IoU quantify where a medical-image segmentation falls short but say nothing about the confidence of each prediction. Over-confident errors are particularly dangerous in clinical practice, where a single false-positive voxel may trigger an unnecessary biopsy. We introduce three contributions that jointly address spatial precision and reliability. (i) Signed-Distance Calibration (SDC) loss couples cross-entropy, local calibration and a differentiable signed-distance penalty, enforcing boundary accuracy while moderating confidence. (ii) A Spatially Adaptive Margin (SAM) module applies lightweight morphological transforms to ground-truth masks before computing the local target, sharpening ambiguous edges. (iii) Pixel-wise Expected Calibration Error (pECE) extends ECE to millions of voxels and penalises high-confidence false positives. Across four public datasets (ACDC, FLARE, BraTS, PROSTATE) and two back-bones (U-Net, nnU-Net), SDC improves Dice by up to 4 percentage points and halves ECE compared with the state of the art, without sacrificing runtime. Code is available on: https://github.com/EagleAdelaide/SDC-Loss.
Federated Continual Recommendation
Authors: Jaehyung Lim, Wonbin Kweon, Woojoo Kim, Junyoung Kim, Seongjin Choi, Dongha Kim, Hwanjo Yu
Venue: CIKM
Year: 2025

The increasing emphasis on privacy in recommendation systems has led to the adoption of Federated Learning (FL) as a privacy-preserving solution, enabling collaborative training without sharing user data. While Federated Recommendation (FedRec) effectively protects privacy, existing methods struggle with non-stationary data streams, failing to maintain consistent recommendation quality over time. On the other hand, Continual Learning Recommendation (CLRec) methods address evolving user preferences but typically assume centralized data access, making them incompatible with FL constraints. To bridge this gap, we introduce Federated Continual Recommendation (FCRec), a novel task that integrates FedRec and CLRec, requiring models to learn from streaming data while preserving privacy. As a solution, we propose F3CRec, a framework designed to balance knowledge retention and adaptation under the strict constraints of FCRec. F3CRec introduces two key components: Adaptive Replay Memory on the client side, which selectively retains past preferences based on user-specific shifts, and Item-wise Temporal Mean on the server side, which integrates new knowledge while preserving prior information. Extensive experiments demonstrate that F3CRec outperforms existing approaches in maintaining recommendation quality over time in a federated environment. Our code is available at https://github.com/Jaehyung-Lim/F3CRec-CIKM-25.
GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction
Authors: Zhijie Lin, Zhuofeng Li, Chenglei Dai, Wentian Bao, Shuai Lin, Enyun Yu, Haoxiang Zhang, Liang Zhao
Venue: CIKM
Year: 2025

In a multi-stage recommendation system, reranking plays a crucial role in modeling intra-list correlations among items. A key challenge lies in exploring optimal sequences within the combinatorial space of permutations. Recent research follows a two-stage (generator-evaluator) paradigm, where a generator produces multiple feasible sequences, and an evaluator selects the best one. In practice, the generator is typically implemented as an autoregressive model. However, these two-stage methods face two main challenges. First, the separation of the generator and evaluator hinders end-to-end training. Second, autoregressive generators suffer from inference efficiency. In this work, we propose a Unified Generative Efficient Reranking Framework (GReF) to address the two primary challenges. Specifically, we introduce Gen-Reranker, an autoregressive generator featuring a bidirectional encoder and a dynamic autoregressive decoder to generate causal reranking sequences. Subsequently, we pre-train Gen-Reranker on the item exposure order for high-quality parameter initialization. To eliminate the need for the evaluator while integrating sequence-level evaluation during training for end-to-end optimization, we propose post-training the model through Rerank-DPO. Moreover, for efficient autoregressive inference, we introduce ordered multi-token prediction (OMTP), which trains Gen-Reranker to simultaneously generate multiple future items while preserving their order, ensuring practical deployment in real-time recommender systems. Extensive offline experiments demonstrate that GReF outperforms state-of-the-art reranking methods while achieving latency that is nearly comparable to non-autoregressive models. Additionally, GReF has also been deployed in a real-world video app Kuaishou with over 300 million daily active users, significantly improving online recommendation quality.
Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation
Authors: Zhutian Lin, Junwei Pan, Haibin Yu, Xi Xiao, Ximei Wang, Zhixiang Feng, Shifeng Wen, Shudong Huang, Dapeng Liu, Lei Xiao
Venue: CIKM
Year: 2025

Multi-domain learning (MDL) has become a prominent topic in enhancing the quality of personalized services. It's critical to learn commonalities between domains and preserve the distinct characteristics of each domain. However, this leads to a challenging dilemma in MDL. On the one hand, a model needs to leverage domain-aware modules such as experts or embeddings to preserve each domain's distinctiveness. On the other hand, real-world datasets often exhibit long-tailed distributions across domains, where some domains may lack sufficient samples to effectively train their specific modules. Unfortunately, nearly all existing work falls short of resolving this dilemma. To this end, we propose a novel Cross-experts Covariance Loss for Disentangled Learning model (Crocodile), which employs multiple embedding tables to make the model domain-aware at the embeddings which consist most parameters in the model, and a covariance loss upon these embeddings to disentangle them, enabling the model to capture diverse user interests among domains. Empirical analysis demonstrates that our method successfully addresses both challenges and outperforms all state-of-the-art methods on public datasets. During online A/B testing in Tencent's advertising platform, Crocodile achieves 0.72% CTR lift and 0.73% GMV lift on a primary advertising scenario. The code is openly accessible at: https://github.com/SkylerLinn/Crocodile.
SC-DAG: Semantic-Constrained Diffusion Attacks for Stealthy Exposure Manipulation in Visually-Aware Recommender Systems
Authors: Ze Lin, Yuqiu Qian, Xiaodong Li, Ziyu Lyu, Hui Li
Venue: CIKM
Year: 2025

Visually-aware recommender system (VARS) has become increasingly prevalent in various online services by integrating visual features of items to enhance recommendation quality. However, VARS introduces new security vulnerabilities and malicious attackers can perform visual shilling attacks to manipulate recommendation lists via uploading generated images with visually imperceptible perturbations. While prior research has explored such threats to help service providers enhance their systems, existing visual shilling attack methods still suffer from uncontrolled pixel-space perturbation, energy dispersion dilemma and semantic misalignment in reference selection. In this work, we present Semantic-Constrained Diffusion Adversarial Generation (SC-DAG) for visual shilling attacks. SC-DAG overcomes key limitations of previous methods by focusing perturbations on semantically meaningful image regions through contour-aware segmentation, guiding adversarial generation in latent space using a conditional diffusion process, and performing a hybrid reference image selection strategy that balances popularity and semantic similarity. Extensive experiments on performing visual shilling attacks against multiple VARS models show that SC-DAG achieves state-of-the-art attack performance in elevating target items' ranking, while maintaining strong perceptual indistinguishability and minimal impact on overall recommendation performance of the system. Our work offers insights into leveraging structured semantic priors for more sophisticated adversarial manipulations against VARS and also highlights the necessity for developing more robust VARS models resilient to visual shilling attacks. We provide our implementation at https://github.com/KDEGroup/SC-DAG.
Position-Agnostic Probabilistic Generation for Robust Steganographic Text
Authors: Shuo Lin, Zhenyang Shen, Xiang Li, Jiacheng Fan, Sixing Wu
Venue: CIKM
Year: 2025

With the rise of linguistic steganography, the robustness to withstand minor perturbations like textual edits or tokenization shifts remains a critical yet crucial challenge. To address this, we propose a novel robustness-enhancing method, Position-Agnostic Probabilistic Generation, which combines semantic clustering with probabilistic generation control. During decoding, the model is softly guided to prefer or avoid specific token sets through time-aware probability boosting, enabling robust bit embedding without relying on fixed token positions. These token sets are constructed from semantically coherent clusters derived from the language model's vocabulary and expanded to ensure fluency. A dynamic, time-aware boosting strategy is then applied to gradually amplify the likelihood of valid tokens throughout the generation. Experimental results demonstrate that our method consistently outperforms baselines in preserving hidden information under 10 types of perturbations.
ConGM: Contrastive Graph Matching for Graph Self-Supervised Learning
Authors: Hongxiang Lin, Lei Wang, Huiying Hu, Xiaoqing Lyu
Venue: CIKM
Year: 2025

Graph neural networks (GNNs) are widely used in information retrieval, but they often require large amounts of labeled data. To address this problem, self-supervised methods like graph contrastive learning (GCL) are developed to learn from graph structures without labeled data. However, GCL faces a challenge in practice. Plenty of traditional GCL methods differ fundamentally from GNNs in handling neighboring nodes, hindering effective contrastive learning. To address the above issue, we propose a graph self-supervised learning model based on Contrastive Graph Matching (ConGM). The model effectively mitigates the conflict between GCL methods and the homophily assumption of GNNs by using linear node matching and quadratic edge alignment mechanisms to treat some neighboring nodes as both positive and negative samples, rather than considering all neighboring nodes as negative samples as in traditional GCL methods. Additionally, to tackle the imbalance of positive and negative samples in edge alignment, we design a bi-level negative sample selection strategy to choose appropriate hard negative samples. Extensive experiments conducted on multiple benchmark datasets have validated the effectiveness of our proposed method.
Stratified Expert Cloning for Retention-Aware Recommendation at Scale
Authors: Chengzhi Lin, Annan Xie, Shuchang Liu, Wuhong Wang, Chuyuan Wang, Yongqi Liu, Han Li
Venue: CIKM
Year: 2025

User retention is critical in large-scale recommender systems, significantly influencing online platforms' long-term success. Existing methods typically focus on short-term engagement, neglecting the evolving dynamics of user behaviors over time. Reinforcement learning (RL) methods, though promising for optimizing long-term rewards, face challenges like delayed credit assignment and sample inefficiency. We introduce Stratified Expert Cloning (SEC), an imitation learning framework that leverages abundant interaction data from high-retention users to learn robust policies. SEC incorporates: 1) multi-level expert stratification to model diverse retention behaviors; 2) adaptive expert selection to dynamically match users with appropriate policies based on their state and retention history; and 3) action entropy regularization to enhance recommendation diversity and policy generalization. Extensive offline evaluations and online A/B tests on major video platforms (Kuaishou and Kuaishou Lite) with hundreds of millions of users validate SEC's effectiveness. Results show substantial improvements, achieving cumulative lifts of 0.098% and 0.122% in active days on the two platforms respectively, each translating into over 200,000 additional daily active users.
EAPformer: Entropy-Aware Patch Transformer for Multivariate Long-Term Time Series Forecasting
Authors: Jiahao Ling, Xuan Yang, Shimin Gong, Bo Gu
Venue: CIKM
Year: 2025

Multivariate long-term time series forecasting is pivotal across numerous domains, yet precise predictions require a differentiated assessment of historical time segments due to their varying influence on future trends. Patch-based Transformer frameworks show promise for capturing local temporal patterns. However, they face limitations with static patching, which disrupts temporal continuity, fails to adapt to shifts between periodic and volatile patterns, and overlooks dynamic interactions between time segments and variables. To address these limitations, we propose Entropy-Aware Patch Transformer (EAPformer) which dynamically segments time series for differentiated assessments of historical patterns. Specifically, we overcome static patching limitations by leveraging temporal entropy to dynamically adjust patch boundaries through a two-stage policy, achieving interpretable and context-sensitive segmentation. Subsequently, we adapt EAPformer to periodic and volatile dynamics by employing entropy-aware segmentation that captures distinct temporal patterns across diverse segments. Finally, we further capture dynamic interactions across time segments and variables by introducing a multi-dimensional dependency learning architecture. Additionally, a gated fusion mechanism integrates local and global patterns, enhancing robustness. Extensive experiments on eight public benchmarks demonstrate that EAPformer outperforms state-of-the-art models, achieving superior accuracy across all metrics.
Seeing Sequences like Humans: Pattern Classification Driven Time-Series Forecasting via Vision Language Models
Authors: Xingyu Liu, Min Gao, Zongwei Wang, Yinbing Bai
Venue: CIKM
Year: 2025

Time-series forecasting is critical to highly data-dependent domains such as energy, healthcare, and transportation. Although Large Language Models have recently been explored for this task, their performance is hindered by a modality gap: numerical sequences poorly align with text-based inputs, and direct alignment often introduces noise. In contrast, human experts rarely predict directly from numbers; they first inspect line charts to recognize overall patterns and then apply simple models for forecasting. Inspired by this workflow, we propose VisMoE, a Vision-Language-Model-driven Mixture-of-Experts framework. In VisMoE, Each sequence is transformed into a line-chart image, enabling a VLM to classify it into distinct temporal regimes. Based on this classification, VisMoE routes the sequence to lightweight specialized experts operating alongside a global predictor, whose outputs are fused for final forecasts. This human-inspired design preserves semantic understanding, reduces modality misalignment, and improves computational efficiency. Extensive experiments across multiple benchmarks demonstrate that VisMoE achieves state-of-the-art forecasting accuracy while remaining highly efficient. Our code is available at https://github.com/Liu905169/VisMoE.