OpenRaiser/Intern-Atlas huggingface dataset:
Local Structure-Adaptive Graph Filtering for Collaborative Filtering
Authors: Yijun Sheng, Ximing Chen, Yanyan Liu, Pui Ieng Lei, Zhiguo Gong
Venue: CIKM
Year: 2025

The structural heterogeneity of user-item interaction graphs poses a fundamental challenge for graph-based recommender systems. While Graph Convolutional Networks (GCNs) have achieved remarkable success in collaborative filtering, their uniform low-pass filtering nature often fails to accommodate the varying spectral needs of nodes with different local structures, resulting in suboptimal performance. To address this issue, we propose a Structurally Sensitive Adaptive Graph Filter, dubbed as SSAGF, a novel framework that enables structure-aware filtering on user-item graphs. SSAGF first clusters nodes based on local structural properties, then learns customized filters per group by reinterpreting the convolutional depth in GCNs. This adaptive mechanism ensures that nodes with distinct structural roles are treated appropriately, enhancing both accuracy and fairness without significantly increasing model complexity. To further improve scalability, SSAGF avoids costly eigenvalue decompositions by approximating spectral filters through Maclaurin series expansion, transforming the convolution into a pooling-like operation over standard GCN outputs. Extensive experiments on four benchmark datasets demonstrate that SSAGF consistently outperforms competitive baselines, especially in scenarios with high structural heterogeneity, offering a principled and efficient solution for structure-aware recommendation.
Learning Invariant Reliability under Diverse Contexts for Robust Multimedia Recommendation
Authors: Yijun Sheng, Pui Ieng Lei, Yanyan Liu, Ximing Chen, Zhiguo Gong
Venue: CIKM
Year: 2025

In graph-based multimedia recommendation, accurately modeling item-item semantic similarity is crucial for constructing high-quality semantic structures. However, multimodal content often exhibits semantic inconsistencies across modalities, resulting in noisy or misleading similarity signals. We refer to this as modality mismatching, where unaligned representations, such as an image conveying semantics unrelated to its accompanying text, undermine the reliability of feature-based similarity estimation. Importantly, modality consistency is context sensitive, varying with the underlying semantic environment in which modalities are interpreted. This highlights the necessity of jointly modeling modality reliability and contextual semantics. To address this challenge, we propose RGSLMRec, a robust graph structure learning framework that models and exploits the semantic reliability of multimodal features under diverse contexts. At its core, RGSLMRec builds on the invariant learning paradigm and introduces two key innovations: (i) it simulates multiple perturbed semantic environments and employs environment-specific monotonic networks to estimate reliability; and (ii) it adopts a risk-invariant objective based on Variance Risk Extrapolation to enforce the learning of invariant reliability across environments. On top of this, (iii) RGSLMRec constructs reliability-guided item-item graphs and captures collaborative and semantic signals via a hybrid early-late fusion strategy. Extensive experiments on several real-world datasets and additional synthetically perturbed datasets demonstrate that RGSLMRec not only outperforms strong baselines but also exhibits superior robustness to modality mismatching.
LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models
Authors: Aleksei Shestov, Omar Zoloev, Maksim Makarenko, Mikhail Orlov, Egor Fadeev, Ivan Kireev, Andrey V. Savchenko
Venue: CIKM
Year: 2025

This paper presents LLM4ES, a novel framework that exploits large pre-trained language models (LLMs) to derive user embeddings from event sequences. Event sequences are transformed into a textual representation, which is subsequently used to fine-tune an LLM through next-token prediction to generate high-quality embeddings. We introduce a text enrichment technique that enhances LLM adaptation to event sequence data, improving representation quality for low-variability domains. Experimental results demonstrate that LLM4ES achieves state-of-the-art performance in user classification tasks in financial and other domains, outperforming existing embedding methods. The resulting user embeddings can be incorporated into a wide range of applications, from user segmentation in finance to patient outcome prediction in healthcare.
Incremental Learning for LLM-based Tokenization and Recommendation
Authors: Haihan Shi, Xinyu Lin, Wenjie Wang, Wentao Shi, Junwei Pan, Jie Jiang, Fuli Feng
Venue: CIKM
Year: 2025

Large Language Models for Recommendation (LLM4Rec) have shown great potential. Many LLM4Rec approaches technically leverage a learnable tokenizer to assign item identifiers and then enable a Recommender LLM (RecLLM) to process tokenized items and user interactions for recommendation. However, a key challenge in their real-world deployment is the need for continuous retraining over time to accommodate new items and evolving user interests. While existing retraining methods can be applied to RecLLMs, learnable tokenizers introduce additional retraining challenges. We conduct a comprehensive investigation into the joint retraining of RecLLMs and learnable tokenizers, identifying key issues such as identifier collision and identifier shifts across periods. To address these, we propose Reformer, an incremental learning framework to fine-tune RecLLMs and learnable tokenizers at each period. Reformer employs a dynamic codebook to mitigate identifier collision by appending new codes and enforcing a diversity-oriented code assignment constraint. Additionally, Reformer adopts an identifier freezing strategy to ensure the invariance of previously assigned item identifiers across retraining periods. We instantiate Reformer on two representative RecLLMs and conduct extensive experiments on three real-world datasets. Substantial results demonstrate its superior retraining performance, facilitating the real-world deployment of LLM4Rec.
Community Partition-based Source Localization with Adaptive Observers Deployment
Authors: Jinchen Shi, Yang Fang, Zhen Tan, Xin Zhang, Xiang Zhao
Venue: CIKM
Year: 2025

In the contemporary era, characterized by an accelerated development in the domain of social networks, the phenomenon of fake news has attained unprecedented levels of prevalence, exerting substantial detrimental influence on society. Identification of the sources of such information in a timely manner is of paramount importance in order to prevent further damage. Existing source localization methods can be categorized into two distinct approaches: the first involves the deployment of observers followed by localization, while the second employs traditional community partitioning for source localization without considering community structure in observer deployment, resulting in suboptimal information acquisition. To address this issue, we propose Community Partition-Based Source Localization with Adaptive Observers Deployment (CSOL), which consists of three stages: In the first stage, community partitioning is achieved using contrastive learning with optimization and a feature extraction module that is highly correlated with partition. In the second stage, we are the first work to adaptively deploy observer based on community importance, integrating community partitioning with observer placement. In the third stage, an early source estimation strategy is employed to enhance efficiency and accuracy. Experimental results in real-world networks demonstrate that CSOL outperforms other SOTA methods in both accuracy and efficiency.
Benefit from Rich: Tackling Search Interaction Sparsity in Search Enhanced Recommendation
Authors: Teng Shi, Weijie Yu, Xiao Zhang, Ming He, Jianping Fan, Jun Xu
Venue: CIKM
Year: 2025

In modern online platforms, search and recommendation (S&R) often coexist, offering opportunities for performance improvement through search-enhanced approaches. Existing studies show that incorporating search signals boosts recommendation performance. However, the effectiveness of these methods relies heavily on rich search interactions. They primarily benefit a small subset of users with abundant search behavior, while offering limited improvements for the majority of users who exhibit only sparse search activity. To address the problem of sparse search data in search-enhanced recommendation, we face two key challenges : (1) how to learn useful search features for users with sparse search interactions, and (2) how to design effective training objectives under sparse conditions. Our idea is to leverage the features of users with rich search interactions to enhance those of users with sparse search interactions. Based on this idea, we propose GSERec, a method that utilizes message passing on the User-Code Graphs to alleviate data sparsity in Search-Enhanced Recommendation. Specifically, we utilize Large Language Models (LLMs) with vector quantization to generate discrete codes, which connect similar users and thereby construct the graph. Through message passing on this graph, embeddings of users with rich search data are propagated to enhance the embeddings of users with sparse interactions. To further ensure that the message passing captures meaningful information from truly similar users, we introduce a contrastive loss to better model user similarities. The enhanced user representations are then integrated into downstream search-enhanced recommendation models. Experiments on three real-world datasets show that GSERec consistently outperforms baselines, especially for users with sparse search behaviors.
SimFormer: Multilevel Transformer on Learnable Mesh Graphs for Engineering Simulation
Authors: Jiasheng Shi, Fu Lin, Weixiong Rao, Ze Gao
Venue: CIKM
Year: 2025

Numerical simulation is important in real-world engineering systems, such as solid mechanics and aero-dynamics. Hierarchical GNNs can learn engineering simulation with low simulation time and acceptable accuracy, but fail to represent complex interactions in simulation systems. In this paper, we propose a novel multilevel Transformer on learnable clusters, namely SimFormer. The key novelty of SimFormer is to interweave the learning of a learnable soft-cluster assignment algorithm and the inter-cluster/cluster-to-node attention. In form of a closed-loop, SimFormer learns the soft cluster assignment possibility by the feedback signals provided by the attention, and the attention can leverage the learnable clusters to better represent long-range interactions. In this way, the learnable clusters can adaptively match actual simulation results, and the multilevel attention modules can also effectively represent node embeddings. Experiments on four datasets demonstrate the superiority of SimFormer over seven baseline approaches. For example, on the real dataset, ours outperforms the recent work Eagle by 17.36% lower RMSE and 27.03% smaller FLOPs. The code and datasets are available at: https://github.com/pro-orp/SimFormer.
Continuous Data Augmentation via Condition-Tokenized Diffusion Transformer for Sequential Recommendation
Authors: Chenglong Shi, Haosen Wang, Pan Tang
Venue: CIKM
Year: 2025

Data augmentation plays a crucial role in enhancing sequential recommendation (SR) by providing richer training signals. Recently, diffusion models (DMs) have been introduced into SR to generate realistic interaction sequences. However, existing DM-based methods face three key limitations: (1) semantic deviation. The rounding procedure, which maps continuous embeddings to discrete item sequences, may introduce semantic deviation; (2) preference misalignment. The explicit preference guidance is neglected during generation, resulting in synthetic sequences that misalign with users' actual interests; (3) suboptimal training strategy. The two-stage methods, which train the SR model and the DM separately, overlook their potential complementarity. To address these challenges, we propose Continuous Data Augmentation via Condition-Tokenized Diffusion Transformer for Sequential Recommendation (CATDiT). Specifically, CATDiT discards the rounding operation and leverages continuous embeddings as augmented data to preserve semantic integrity. Then, we guide the generation process with user intent via a condition-tokenized Diffusion Transformer, aligning synthetic sequences with users' real preferences. Finally, we propose an alternating optimization strategy to enable mutual learning between the SR model and the DM. Extensive experiments on five real-world datasets demonstrate that CATDiT consistently outperforms state-of-the-art baselines, validating its effectiveness in generating high-quality sequences and improving SR performance.
MSOFormer: Multi-scale Transformer with Orthogonal Embedding and Frequency Modeling for Multivariate Time Series Forecasting
Authors: Qin Shi, Chu Xu, Zongtang Hu, Dong Shen, Dapeng Sun, Lijun Quan
Venue: CIKM
Year: 2025

Multivariate Time Series Forecasting (MTSF) plays a critical role in diverse practical applications. Although Transformer-based models have recently achieved impressive results in this field, their performance is still hindered by three core challenges: complex temporal dependencies, diverse inter-variable correlations, and patterns that span multiple time scales. To address these issues, we propose MSOFormer-a Multi-scale Transformer with Orthogonal Embedding and Frequency Modeling. Specifically, the Dynamic Frequency Filter adaptively weights frequency components across variables based on input characteristics, enabling full-spectrum modeling and precise extraction of key frequency patterns. To improve inter-variable representation, we introduce Orthogonal Embedding, a novel projection strategy for queries and keys that enhances feature diversity in channel-wise self-attention. In addition, Multi-scale Patch Embedding captures temporal features across different scales, providing a comprehensive time series representation. To evaluate MTSF in cloud-native environments, we construct the first three Cloud Kafka cluster datasets, specifically curated for elastic message queue scaling scenarios. Extensive experiments across eleven real-world benchmark datasets demonstrate that MSOFormer consistently outperforms existing state-of-the-art methods, highlighting its effectiveness and broad applicability.
Company-Specific Knowledge Matters: Retrieval-Augmented Generation for Earnings Call Answer Rehearsal
Authors: Yung-Yu Shih, Yun-Nung Chen, Chung-Chi Chen
Venue: CIKM
Year: 2025

Retrieval-augmented generation (RAG) has long been used to guide generative models in producing more accurate answers, with most discussions focusing on reading comprehension-based question answering (QA). However, their role in real-world answer rehearsal scenarios remains underexplored. The rise of large language models (LLMs) presents new opportunities to develop systems that assist professionals, making this discussion both timely and essential. This paper explores how to better support corporate executives in answering questions from professional analysts during earnings calls. We compare the impact of two external knowledge sources: large-scale causal knowledge graphs (KGs) and historical Q&A records-retrieved either from a global pool or company-specific archives. Our findings suggest that a company's historical Q&A records are more influential than causal KGs in improving response quality. To the best of our knowledge, this is the first study to systematically compare and analyze different knowledge resources in answer rehearsal. Our findings show the potential of inspiring future research on the interplay between KG and historical QA pairs for answer rehearsal.
Zero-shot Stroke Lesion Segmentation via CAM-guided Prompting of MedSAM2
Authors: Mohammad Javad Shokri, Yuchong Yao, Nandakishor Desai, Aravinda S. Rao, Angelos Sharobeam, Bernard Yan, Marimuthu Palaniswami
Venue: CIKM
Year: 2025

Accurate segmentation of stroke lesions in diffusion-weighted imaging (DWI) is crucial for clinical decision-making. However, automated infarct segmentation remains challenging due to variable infarct sizes and locations, and it is labor-intensive, requiring expert manual annotations for training. We propose a zero-shot framework to eliminate the need for manual segmentation labels by leveraging weak supervision from class activation maps (CAMs) to guide segmentation using MedSAM2, a foundation model for 3D medical image segmentation. By extracting attention maps from a fine-tuned ResNet on DWI scans labeled with stroke etiology (cause) and combining them with intensity information, we identify key regions and generate bounding-box prompts for MedSAM2. Our method achieves a Dice score of 54.2 ± 5.3% without any manual segmentation labels or tuning of the MedSAM2 model, demonstrating its potential as a scalable solution for reliable pseudo-label generation.
LinkML for Collaborative Petrochemical Knowledge Graph Development
Authors: Annie Shoup, Adam Russell, Gavin Nicol
Venue: CIKM
Year: 2025

LinkML is an emerging ontology modeling framework using YAML syntax instead of the typical semantic web technologies such as OWL. LinkML's approachable syntax allows a wide range of stakeholders-including domain experts, software engineers, and data scientists-to collaboratively define and refine a shared semantic model. We leveraged LinkML to develop a 1,200+ class ontology capturing complex physical infrastructure, emissions sources, equipment relationships, and time-series observations critical to source-level emissions attribution. We developed tooling to automate transformation of LinkML into graph database schemas, entity-relationship (ER) diagrams, documentation, and typed domain model code, supporting rapid iteration and semantic consistency across systems. The resulting large-scale knowledge graph, deployed in a property graph database, materializes digital twins for over 700 petrochemical facilities and more than 61,000 pieces of equipment. This approach dramatically simplifies emissions analytics by standardizing data integration and replacing fragile, bespoke SQL logic with intuitive graph queries. The accessibility of LinkML enables domain experts to directly contribute to the model, while providing a robust foundation for engineers and data scientists to perform scalable, reliable analytics. The result is a unified platform for emissions reporting and digital transformation. This paper presents the use of LinkML for semantic modeling for emissions reporting in the petrochemical industry, the tooling supporting its deployment, and the challenges and successes of the approach.
Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration
Authors: Amitay Sicherman, Kira Radinsky
Venue: CIKM
Year: 2025

Computational prediction of enzymatic reactions represents a crucial challenge in sustainable chemical synthesis across various scientific domains, ranging from drug discovery to materials science and green chemistry. These syntheses rely on highly adaptable protein catalysts that perform different molecular transformations depending on their molecular partners. Current approaches to protein representation in reaction prediction either ignore protein structure entirely or rely on static embeddings, failing to capture how proteins dynamically adapt their behavior to different substrates. We present Docking-Aware Attention (DAA), a novel architecture that generates dynamic, context-dependent protein representations by incorporating molecular docking information into the attention mechanism. DAA combines physical interaction scores from docking predictions with learned attention patterns to focus on protein regions most relevant to specific molecular interactions. We evaluate our method on enzymatic reaction prediction, where it outperforms previous state-of-the-art methods, demonstrating a 9.5% relative improvement on complex molecules and a 12.3% relative improvement on innovative reactions. Furthermore, we demonstrate the generalization capabilities of our learned representations of a Drug-Target Interaction. We demonstrate how DAA generates interpretable attention patterns that adapt to different molecular contexts through detailed ablation studies and visualizations. Our approach represents a general framework for context-aware protein representation, with potential applications across enzymatic synthesis planning and other protein-molecule interaction tasks. We open-source our implementation and pre-trained models to facilitate further research.
InstANNS: Scalable Approximate Nearest Neighbor Search via Cost-Efficient In-Storage Processing
Authors: Bonggeun Sim, Yushin Kim, Minseo Kim, Yeonhong Park, Jae W. Lee
Venue: CIKM
Year: 2025

Billion-scale approximate nearest neighbor search (ANNS) increasingly relies on disk-based indexes due to the rapid growth of modern datasets. Existing disk-augmented indexing systems, such as SPANN, often face performance bottlenecks due to limited host interface bandwidth, typically constrained by PCIe. To address this bottleneck, we introduce InstANNS, a storage-centric ANNS architecture that improves throughput and reduces data transfer by performing query-aware PQ filtering inside SSDs, without relying on GPUs. By offloading distance computations to the SSD controller and utilizing abundant internal bandwidth, our design transfers only highly relevant candidates to the host, significantly reducing PCIe traffic. To further optimize performance, we propose co-occurrence-aware PQ code placement, which co-locates frequently co-accessed candidates, and conditional PQ bypass, which reduces NAND reads by skipping low-utility filtering. We prototype InstANNS by extending SPANN and evaluate it using a device-level SSD simulator, meticulously calibrated against measurements from an actual SSD controller SoC (System-on-Chip) to ensure accurate performance evaluation. Experimental results show that InstANNS improves QPS by 2.15× over SPANN and QPS-per-dollar by 1.7× over FusionANNS at 90% Recall@10, while maintaining accuracy.
L3X: Long Object List Extraction from Long Documents
Authors: Sneha Singhania, Simon Razniewski, Gerhard Weikum
Venue: CIKM
Year: 2025

Information extraction with LLMs is typically geared toward extracting individual subject-predicate-object (SPO) triples from short factual texts such as Wikipedia or news articles. In contrast, the L3X methodology tackles the task of extracting long lists from long texts: given a target subject S and predicate P, the goal is to extract the complete list of all objects O for which SPO holds. This is especially challenging over long texts, like entire books or large web crawls, where many objects are long-tail entities. We demonstrate L3X, a web-based system designed for this previously unexplored task. L3X comprises of recall-oriented candidate generation using LLMs in RAG mode, with novel methods for ranking and batching passages, followed by precision-oriented scrutinization. Our demo supports exploring multiple configurations, including LLM-only and RAG baselines, showcasing use cases like fiction-character relations from book series (e.g., 50+ friends of Harry Potter) and business relations from web pages (e.g., CEOs of Toyota).