OpenRaiser/Intern-Atlas huggingface dataset:
Monte Carlo Tree Search for Graph Reasoning in Large Language Model Agents
Authors: Lihui Liu
Venue: CIKM
Year: 2025

While large language models (LLMs) have achieved impressive results across many tasks, they remain prone to hallucinations, particularly in domains requiring substantial background knowledge. A common way to mitigate this issue is to incorporate external knowledge, often through retrieval-augmented generation (RAG). However, most existing RAG approaches focus solely on textual data and neglect an important aspect: the connections between pieces of knowledge. In domains such as scientific publishing, entities like papers, authors, and citations form rich graphs, where meaning emerges not only from individual texts but also from their relationships. To address this, we propose Graph-MCTS, a framework that enhances LLM reasoning by leveraging graph structures. Graph-MCTS uses Monte Carlo Tree Search (MCTS) to guide the model through structured exploration of graph-based knowledge. We evaluate Graph-MCTS across multiple LLM architectures and find that it consistently outperforms existing augmentation methods. These findings highlight the importance of structured, relational knowledge for improving the reasoning capabilities of LLMs. Code is available at https://github.com/lihuiliullh/Graph-MCTS
Masked Graph Distance Network for Accurate Subgraph Similarity Computation
Authors: Xijuan Liu, Yin Chen, Fan Li, Xiaoyang Wang, Haiyang Hu, Ying Zhang
Venue: CIKM
Year: 2025

Subgraph similarity search aims to identify target graphs in the database that approximately contain the query graph, which is a fundamental problem in graph analysis. As a key measure for subgraph similarity computation, Subgraph Edit Distance (SED) has garnered significant research attention. Unfortunately, the exact computation of SED is an NP-hard problem. In recent years, some studies have attempted to leverage Graph Neural Networks (GNNs) to learn SED. However, existing GNN-based methods suffer from two significant limitations: (1) They rely on node-centric message passing, which cannot fully capture the impact of graph topology changes caused by graph edit operations. (2) They struggle to handle the asymmetry of SED, making it challenging to balance the scale differences between the input graphs and their distances in the representation space. To address these issues, this paper proposes a novel Masked Graph Distance Network (MGDN) for accurate SED approximation. First, MGDN utilizes a unified graph encoder to perform message passing based on the original graph structure and its dual hypergraph, effectively capturing the impact of node- and edge-specific edits. Then, we introduce an adaptive graph masking module that flexibly assigns masking scores to nodes and edges in the target graph to address asymmetry. Using multi-head masking, we re-encode the input graphs to focus on substructures relevant to SED computation. Finally, a multi-view predictor is employed at the graph level to approximate the SED, enhancing estimation accuracy by integrating information from multiple perspectives. Extensive experiments on nine benchmark datasets demonstrate that MGDN significantly outperforms state-of-the-art methods.
ECKGBench: Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graph
Authors: Langming Liu, Haibin Chen, Yuhao Wang, Yujin Yuan, Shilei Liu, Wenbo Su, Xiangyu Zhao, Bo Zheng
Venue: CIKM
Year: 2025

Large language models (LLMs) have demonstrated their capabilities across various natural language processing (NLP) tasks. Their potential in e-commerce is also substantial, evidenced by existing implementations in scenarios such as platform search and recommender systems. One obstinate concern associated with LLMs is the factuality issue (e.g., hallucination), which is urgent in e-commerce due to its significant impact on user experience and revenue. While some methods aim to evaluate the factuality of LLMs, issues such as lack of objectivity, high consumption, and lack of domain expertise arise. To this end, leveraging a collected knowledge graph (KG) as a reliable source, we propose ECKGBench, a question-answering dataset to assess LLMs' capacity in e-commerce. Specifically, each question is automatically generated based on one KG triple through a standardized pipeline, guaranteeing evaluation quality and reliability. We evaluate advanced LLMs using ECKGBench and provide insights into experimental results. The dataset is available online at~ https://github.com/OpenStellarTeam/ECKGBench.
AdaPatch: Adaptive Patch-Level Modeling for Non-Stationary Time Series Forecasting
Authors: Kun Liu, Zhongjie Duan, Cen Chen, Yanhao Wang, Dawei Cheng, Yuqi Liang
Venue: CIKM
Year: 2025

Time series forecasting has witnessed significant advancements through deep learning techniques. However, most existing methods struggle in non-stationary environments, where data distributions evolve over time due to concept drift. To address the challenge of non-stationarity in time series, various stabilization techniques have been proposed to mitigate temporal variations. Nonetheless, these methods operate at the instance level, assuming a homogeneous distribution across all time steps within an instance and relying on fixed statistical normalization. This limits their ability to effectively capture fine-grained distributional shifts. In this paper, we introduce AdaPatch, a novel forecasting model specifically designed to tackle non-stationary multivariate time series. AdaPatch addresses intra-instance distributional shifts by adopting an adaptive scheme for patch-level encoding and normalization, which makes the model capture fine-grained temporal variations more effectively. To further enhance the quality of representations, AdaPatch incorporates a patch reconstruction branch and jointly optimizes a reconstruction loss alongside the forecasting objective. This auxiliary path serves as an implicit regularization mechanism, guiding the encoder to retain meaningful local temporal structures. Furthermore, to enable AdaPatch to better model complex local dynamics, we propose a patch-based predictive decoding strategy that leverages the decoder from the reconstruction branch to replace conventional point-wise forecasting with a more structured patch-level prediction mechanism. Extensive experiments conducted on six real-world multivariate time series datasets demonstrate that AdaPatch achieves superior performance compared to several state-of-the-art baselines, highlighting its effectiveness and strong generalization capability. Our code and data are publicly available at https://github.com/iuaku/AdaPatch.
AutoDW-TS: Automated Data Wrangling for Time-Series Data
Authors: Lei Liu, So Hasegawa, Shailaja Keyur Sampat, Mehdi Bahrami, Wei-Peng Chen, Kodai Toyota, Takashi Kato, Takumi Akazaki, Akira Ura, Tatsuya Asai
Venue: CIKM
Year: 2025

Data wrangling - the process of preparing raw data for analysis through cleansing, transformation, and enrichment - is a critical step in the data science pipeline. Its importance is amplified for time-series data, which underpins many applications, with forecasting being one of the most prominent tasks. Yet, current practices remain largely manual, time-consuming, and error-prone, limiting productivity and scalability. In this paper, we introduce AutoDW-TS, an automated approach to time-series data wrangling powered by Large Language Models (LLMs). Our method offers an end-to-end pipeline, automating key stages such as table merging, prediction engineering, cleansing, imputation, and enrichment. To support diverse use cases, we developed multiple systems, including an interactive AutoDW-TS WebApp, Web APIs, and an AI agent. We share insights from developing and deploying these systems, along with results from an extensive evaluation across 38 time-series benchmarks. Our findings show that AutoDW-TS significantly improves forecasting performance, demonstrating its effectiveness and potential to transform time-series data preparation at scale.
Improved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback
Authors: Kejin Liu, Junhong Lian, Xiang Ao, Ningtao Wang, Xing Fu, Yu Cheng, Weiqiang Wang, Xinyu Liu
Venue: CIKM
Year: 2025

Accurate personalized headline generation hinges on precisely capturing user interests from historical behaviors. However, existing methods neglect personalized-irrelevant click noise in entire historical clickstreams, which may lead to hallucinated headlines that deviate from genuine user preferences. In this paper, we reveal the detrimental impact of click noise on personalized generation quality through rigorous analysis in both user and news dimensions. Based on these insights, we propose a novel Personalized Headline Generation framework via Denoising Fake Interests from Implicit Feedback (PHG-DIF). PHG-DIF first employs dual-stage filtering to effectively remove clickstream noise, identified by short dwell times and abnormal click bursts, and then leverages multi-level temporal fusion to dynamically model users' evolving and multi-faceted interests for precise profiling. Moreover, we release DT-PENS, a new benchmark dataset comprising the click behavior of 1,000 carefully curated users and nearly 10,000 annotated personalized headlines with historical dwell time annotations. Extensive experiments demonstrate that PHG-DIF substantially mitigates the adverse effects of click noise and significantly improves headline quality, achieving state-of-the-art (SOTA) results on DT-PENS. Our framework implementation and dataset are available at https://github.com/liukejin-up/PHG-DIF.
CSMD: Curated Multimodal Dataset for Chinese Stock Analysis
Authors: Yu Liu, Zhuoying Li, Ruifeng Yang, Fengran Mo, Cen Chen
Venue: CIKM
Year: 2025

The stock market is a complex and dynamic system, where it is non-trivial for researchers and practitioners to uncover underlying patterns and forecast stock movements. The existing studies for stock market analysis rely on leveraging various types of information to extract useful factors, which are highly conditional on the quality of the data used. However, the currently available resources are mainly based on the U.S. stock market in English, which is inapplicable to adapt to other countries. To address these issues, we propose CSMD, a multimodal dataset curated specifically for analyzing the Chinese stock market with meticulous processing for validated quality. In addition, we develop a lightweight and user-friendly framework LightQuant for researchers and practitioners with expertise in financial domains. Experimental results on top of our datasets and framework with various backbone models demonstrate their effectiveness compared with using existing datasets. The datasets and code are publicly available at the link: https://github.com/ECNU-CILAB/LightQuant.
LLMCBR: Large Language Model-based Multi-View and Multi-Grained Learning for Bundle Recommendation
Authors: Shiqin Liu, Chaozhuo Li, Minjun Zhao, Litian Zhang, Jiajun Bu
Venue: CIKM
Year: 2025

The exploration of bundle recommendation has garnered significant attention for its potential to enhance user experience and augment business sales. Previous research in this domain has primarily focused on modeling user-item and user-bundle interactions, utilizing multi-view collaboration to bolster the accuracy of bundle recommendations. Nevertheless, existing methodologies exhibit limitations, notably in the inadequate modeling of multi-view information and the absence of multi-grained details. Consequently, addressing the intricate correlation among users, items, and bundles necessitates a sophisticated approach capable of capturing both global and local nuances. We present a novel framework named Large Language Model-based Multi-View and Multi-Grained Learning for Bundle Recommendation (LLMCBR). We introduce an LLM-based semantic refinement module to summarize and encode bundle-level knowledge. To bridge the gap between semantic representation and collaborative signals, we design an adaptation strategy. Furthermore, LLMCBR leverages multi-view and multi-granular modeling to unify collaborative signals. Specifically, LLMCBR integrates item preferences within both bundle-view and item-view, thereby augmenting the comprehensiveness of multi-view data. Following this integration, each view undergoes stratification into multiple granularities to facilitate the acquisition of multi-grained details. We introduce a multiple contrastive instance mechanism to regulate the influence of different granularities and views. This mechanism empowers the model to comprehend complex consumer behaviors across various dimensions. LLMCBR is extensively evaluated over three real-world datasets, and the experimental results demonstrate its superiority.
Enabling Group Fairness in Machine Unlearning via Distribution Correction
Authors: Yezi Liu, Yanning Shen
Venue: CIKM
Year: 2025

Machine unlearning is a recently developed technique to remove the influence of specific data points from a trained model. However, most machine unlearning approaches focus on preserving model performance, which may inadvertently introduce bias. From a preliminary study, we found that a model can become more biased after applying unlearning algorithms. To address this issue, we propose FMU (Fair Machine Unlearning), which ensures group fairness throughout the unlearning process. Specifically, FMU first withdraws the model updates for batches containing unlearning requests to protect privacy. It then removes model updates from additional sampled batches that carry reversed sensitive attributes linked to the same requests, mitigating newly introduced bias. Our experiments compare FMU with standard machine unlearning baselines and one fair unlearning method. Results show that FMU achieves superior fairness while maintaining privacy and delivering accuracy comparable to full retraining. Furthermore, FMU remains effective across diverse unlearning requests involving varying data distributions. Being orthogonal to specific unlearning and debiasing techniques, FMU provides a flexible foundation for more advanced fair machine unlearning research.
Token-Fusion: A Sparse Expert Routing Method for Multi-task Data Matching
Authors: Fangnuo Liu, Derong Shen
Venue: CIKM
Year: 2025

Multi-task data matching-including entity matching, entity linking, and schema matching-is a fundamental task in data integration, yet remains challenging due to heterogeneous inputs and task-specific model designs. We propose Token-Fusion, a unified sparse expert method that integrates token-level dynamic expert routing, adaptive expert pool management, and a fusion strategy guided by both confidence and performance gain. Meanwhile, regularization losses are designed to encourage sparse and diverse expert activation for improved efficiency. Our extensive experimental evaluation on six public datasets demonstrates the effectiveness and efficiency of Token-Fusion in handling heterogeneous matching tasks, establishing it as a promising solution for unified and scalable multi-task data matching.
Few-Shot Knowledge Graph Completion via Transfer Knowledge from Similar Tasks
Authors: Lihui Liu, Zihao Wang, Dawei Zhou, Ruijie Wang, Yuchen Yan, Bo Xiong, Sihong He, Hanghang Tong
Venue: CIKM
Year: 2025

Knowledge graphs (KGs) are essential in many AI applications but often suffer from incompleteness, limiting their utility. Many relations in KGs have only a few examples, making it challenging to train accurate models. Few-shot learning offers a promising direction by enabling KG completion with only a small number of training triplets. However, most existing approaches treat each relation independently and fail to leverage shared information across tasks. In this paper, we introduce TransNet, a transfer learning method for few-shot KG completion that captures task relationships and reuses knowledge from related tasks. TransNet further incorporates meta-learning to effectively handle unseen relations. Experiments on standard benchmarks demonstrate that TransNet achieves strong performance compared to prior methods. Code and data will be released upon acceptance.
A Soft-partitioned Semi-supervised Collaborative Transfer Learning Approach for Multi-Domain Recommendation
Authors: Xiaoyu Liu, Yiqing Wu, Ruidong Han, Fuzhen Zhuang, Xiang Li, Wei Lin
Venue: CIKM
Year: 2025

n industrial practice, Multi-domain Recommendation (MDR) plays a crucial role. Shared-specific architectures are widely used in industrial solutions to capture shared and unique attributes via shared and specific parameters. However, with imbalanced data across different domains, these models face two key issues: (1) Overwhelming: Dominant domain data skews model performance, neglecting non-dominant domains. (2) Overfitting: Sparse data in non-dominant domains leads to overfitting in specific parameters. To tackle these challenges, we propose Soft-partitioned Semi-supervised Collaborative Transfer Learning (SSCTL) for multi-domain recommendation. SSCTL generates dynamic parameters to address the overwhelming issue, thus shifting focus towards samples from non-dominant domains. To combat overfitting, it leverages pseudo-labels with weights from dominant domain instances to enhance non-dominant domain data. We conduct comprehensive experiments, both online and offline, to validate the efficacy of our proposed method. Online tests yielded significant improvements across various domains, with increases in GMV ranging from 0.54% to 2.90% and enhancements in CTR ranging from 0.22% to 1.69%.
Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval
Authors: Yuxiang Liu, Tian Wang, Gourab Kundu, Tianyu Cao, Guang Cheng, Zhen Ge, Jianshu Chen, Qingjun Cui, Trishul Chilimbi
Venue: CIKM
Year: 2025

Transformer-based models such as BERT and E5 have significantly advanced text embedding by capturing rich contextual representations. However, many complex real-world queries require sophisticated reasoning to retrieve relevant documents beyond surface-level lexical matching, where encoder-only retrievers often fall short. Decoder-only large language models (LLMs), known for their strong reasoning capabilities, offer a promising alternative. Despite this potential, existing LLM-based embedding methods primarily focus on contextual representation and do not fully exploit the reasoning strength of LLMs. To bridge this gap, we propose Reasoning-Infused Text Embedding (RITE), a simple but effective approach that integrates logical reasoning into the text embedding process using generative LLMs. RITE builds upon existing language model embedding techniques by generating intermediate reasoning texts in the token space before computing embeddings, thereby enriching representations with inferential depth. Experimental results on BRIGHT, a reasoning-intensive retrieval benchmark, demonstrate that RITE significantly enhances zero-shot retrieval performance across diverse domains, underscoring the effectiveness of incorporating reasoning into the embedding process.
Enhancing Recommendation with Reliable Multi-profile Alignment and Collaborative-aware Contrastive Learning
Authors: Yibin Liu, Jianyu Zhang, Shijian Li
Venue: CIKM
Year: 2025

Recent studies have explored the integration of Large Language Models (LLMs) into recommender systems to enhance the semantic understanding of users and items. While traditional collaborative filtering approaches primarily rely on interaction histories, LLM-enhanced methods attempt to construct comprehensive profiles by leveraging descriptive metadata and user-generated reviews. The semantic representations of these profiles are then aligned with recommender embeddings to enhance the performance of recommender systems. However, the effectiveness of such approaches heavily depends on the quality of the generated profiles, which face several critical challenges: inaccurate profiles, insufficient information and information gap between semantic representations and recommender embeddings. To tackle these challenges, we propose a novel framework with reliable multi-profile alignment and collaborative-aware contrastive learning. Specifically, we introduce a profile generation method combining Chain-of-Thought(CoT) prompting and self-reflection to address the issue of inaccurate profiles. To alleviate the problem of insufficient information, we introduce an interactive profile construction mechanism that aggregates and summarizes common characteristics from users' and items' neighbors in the user-item graph. To bridge the information gap between semantic representations and recommender embeddings, we propose interactive information fusion(IIF), which aggregates semantic representations from neighbors and employs supervised contrastive learning to guide representation learning. Furthermore, we propose a multi-profile alignment framework that aligns recommender embeddings with both basic profiles and interactive profiles through deduplicated contrastive objectives, facilitating effective semantic-behavioral alignment. Extensive experiments on three public datasets and six base recommenders demonstrate that our method consistently outperforms strong LLM-based baselines, achieving an average improvement of 2.93% in Recall@20 and 2.64% in NDCG@20.
Structure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation
Authors: Zhen Liu, Yongtao Zhang, Shaobo Ren, Yuxin You
Venue: CIKM
Year: 2025

Graph domain adaptation has gained significant attention in label-scarce scenarios across different graph domains. Traditional approaches to graph domain adaptation primarily focus on transforming node attributes over raw graph structures and aligning the distributions of the transformed node features across networks. However, these methods often struggle with the underlying structural heterogeneity between distinct graph domains, which leads to suboptimal distribution alignment. To address this limitation, we propose Structure-Attribute Transformation with Markov Chain (SATMC), a novel framework that sequentially aligns distributions across networks via both graph structure and attribute transformations. To mitigate the negative influence of domain-private information and further enhance the model's generalization, SATMC introduces a private domain information reduction mechanism and an empirical Wasserstein distance. Theoretical proofs suggest that SATMC can achieve a tighter error bound for cross-network node classification compared to existing graph domain adaptation methods. Extensive experiments on nine pairs of publicly available cross-domain datasets show that SATMC outperforms state-of-the-art methods in the cross-network node classification task. The code is available at https://github.com/GiantZhangYT/SATMC.
Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning
Authors: Andreas Loizou, Dimitrios Tsoumakos
Venue: CIKM
Year: 2025

As the volume and diversity of available datasets continue to increase, assessing data quality has become crucial for reliable and efficient Machine Learning analytics. A modern, game-theoretic approach for evaluating data quality is the notion of Data Shapley which quantifies the value of individual data points within a dataset. State-of-the-art methods to scale the NP-hard Shapley computation also face severe challenges when applied to large-scale datasets, limiting their practical use. In this work, we present a Data Shapley approach to identify a dataset's high-quality data tuples, Chunked Data Shapley (C-DaSh). C-DaSh scalably divides the dataset into manageable chunks and estimates the contribution of each chunk using optimized subset selection and single-iteration stochastic gradient descent. This approach drastically reduces computation time while preserving high quality results. We empirically benchmark our method on diverse real-world classification and regression tasks, demonstrating that C-DaSh outperforms existing Shapley approximations in both computational efficiency (achieving speedups between 80× - 2300×) and accuracy in detecting low-quality data regions. Our method enables practical measurement of dataset quality on large tabular datasets, supporting both classification and regression pipelines.