OpenRaiser/Intern-Atlas huggingface dataset:
Rethinking Continual Knowledge Graph Embedding: Benchmarks and Analysis
Authors: Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru, Qika Lin, Yuxia Geng, Haiping Zhu, Yudai Pan, Jun Liu
Venue: SIGIR
Year: 2025

Continual knowledge graph embedding (CKGE) has gained wide attention for managing dynamic knowledge graphs (KGs), which are continuously updated with new facts. Unlike traditional methods designed for static KGs, CKGE enables incremental updates to KG embeddings to accommodate new facts while retaining previously learned knowledge. Despite these advancements, current CKGE studies and benchmarks primarily focus on handling the increasing scale of data while overlooking changes in graph patterns. These changes, altering the graph structure of KGs, are referred to as pattern shifts in this paper. Pattern shifts frequently arise as new facts are added, introducing significant challenges to the stability and adaptability of CKGE methods. To address this gap, we introduce a suite of novel and challenging benchmarks, called PS-CKGE, specifically designed to evaluate CKGE methods under pattern shifts, where logic rules are utilized to capture and manage structural changes in dynamic KGs. Through these benchmarks, we comprehensively evaluate current CKGE methods in terms of their overall performance, resistance to catastrophic forgetting, and adaptability to new knowledge. The results show that pattern shifts not only exacerbate their risk of catastrophic forgetting but also impair their adaptability, usually with greater performance degradation over triples associated with more significant changes.
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation
Authors: Yuchuan Zhao, Tong Chen, Junliang Yu, Kai Zheng, Lizhen Cui, Hongzhi Yin
Venue: SIGIR
Year: 2025

Sequential recommender systems (SRSs) excel in capturing users' dynamic interests, thus playing a key role in various industrial applications. The popularity of SRSs has also driven emerging research on their security aspects, where data poisoning attack for targeted item promotion is a typical example. Existing attack mechanisms primarily focus on increasing the ranks of target items in the recommendation list by injecting carefully crafted interactions (i.e., poisoning sequences), which comes at the cost of demoting users' real preferences. Consequently, noticeable recommendation accuracy drops are observed, restricting the stealthiness of the attack. Additionally, the generated poisoning sequences are prone to substantial repetition of target items, which is a result of the unitary objective of boosting their overall exposure and lack of effective diversity regularizations. Such homogeneity not only compromises the authenticity of these sequences, but also limits the attack effectiveness, as it ignores the opportunity to establish sequential dependencies between the target and many more items in the SRS. To address the issues outlined, we propose a Diversity-aware Dual-promotion Sequential Poisoning attack method named DDSP for SRSs. Specifically, by theoretically revealing the conflict between recommendation and existing attack objectives, we design a revamped attack objective that promotes the target item while maintaining the relevance of preferred items in a user's ranking list. We further develop a diversity-aware, auto-regressive poisoning sequence generator, where a re-ranking method is in place to sequentially pick the optimal items by integrating diversity constraints. By attacking two representative SRSs on three real-world datasets, comprehensive experimental results demonstrate that DDSP outperforms state-of-the-art attack methods in attack effectiveness. Moreover, DDSP achieves the strongest stealthiness with its lowest impact on recommendation accuracy.
How Cohesive Are Community Search Results on Online Social Networks?: An Experimental Evaluation
Authors: Yining Zhao, Sourav S. Bhowmick, Nastassja L. Fischer, S. H. Annabel Chen
Venue: SIGIR
Year: 2025

Recently, numerous community search methods for large graphs have been proposed, at the core of which is defining and measuring cohesion. This paper experimentally evaluates the effectiveness of these community search algorithms w.r.t. cohesiveness in the context of online social networks. Social communities are formed and developed under the influence of group cohesion theory, which has been extensively studied in social psychology. However, current generic methods typically measure cohesiveness using structural or attribute-based approaches and overlook domain-specific concepts such as group cohesion. We introduce five novel psychology-informed cohesiveness measures, based on the concept of group cohesion from social psychology, and propose a novel framework called CHASE for evaluating eight representative community search algorithms w.r.t. these measures on online social networks. Our analysis reveals that there is no clear correlation between structural and psychological cohesiveness, and no algorithm effectively identifies psychologically cohesive communities in online social networks. This study provides new insights that could guide the development of future community search methods.
HCDS: Hierarchical Clustering for Cold-Start Few-Shot Data Selection
Authors: Yuhua Zhao, Zhixin Han, Xunzhi Wang, Bitong Luo, Hang Gao, Minlie Huang, Mengting Hu
Venue: SIGIR
Year: 2025

Deep learning models usually require large labeled datasets to generalize well, but this is computationally and financially costly. Cold-start few-shot data selection enables fast model generalization by selecting a few diverse, representative samples from an unlabeled data pool. To achieve this goal, previous work usually divides the training data into several clusters and performs sampling from these clusters. Yet, such a way tends to have two issues. First, imbalanced data distribution in the training data pool still exists in the selected subset, causing models' performance biases and suboptimal generalization ability. Second, these methods improve sample diversity in each cluster by considering either the feature dissimilarity among instances, or model uncertainty for individual instance. They ignore the entire representativeness of samples within a cluster. To tackle these challenges, we propose a novel framework HCDS : Hierarchical Clustering for Cold-Start Few-Shot Data Selection. Specifically, we first perform class-level clustering, using pseudo-labels for class supervision and applying contrastive clustering to derive class-rich features. We then refine these features within the class-level clusters into semantically meaningful features and perform representation-level clustering. Finally, we sample data from the representation-level clusters based on global similarity to ensure representativeness. Experimental results on six public datasets, including both balanced and imbalanced ones, show that HCDS achieves state-of-the-art performance, particularly with limited and imbalanced data.
Ontology-Guided Knowledge Graph Retrieval for Multi-Hop and Cross-Granularity Store Fulfillment Queries
Authors: Mengyue Zhao, Matthew S. Nokleby, Bo Shen, Wenbo Dong, Deepti Pachauri, Andrew Yang
Venue: SIGIR
Year: 2025

Answering complex queries in store fulfillment, such as ''What percentage of employee-assigned actions remain unresolved?'' or ''How many worklists for a specific product type were completed within a timeframe at each location?'' requires precise, multi-hop reasoning across datasets with varying granularities. This paper introduces an ontology-based knowledge graph (KG) approach integrated with a structured text-to-Cypher generation pipeline, enabling accurate retrieval for such queries. Benchmarking against a robust hybrid search baseline combining BM25 and semantic search, our method demonstrates superior performance in addressing multi-hop and cross-granularity questions. Leveraging a KG schema designed to capture intricate relationships (e.g. (OrderLineItem)-[:INVOLVES_ACTION]->(Action)-[:INVOLVES]->(BatchProcess)-[:IS_COMPLETED_AT]->(Location)), we reveal universal patterns for constructing and querying highly relational data. This work highlights the transformative potential of ontology-driven KGs to improve reasoning, data aggregation, and decision-making, with broader implications for any domain requiring structured, multi-relational data analysis.
Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure
Authors: Ziqi Zhao, Zhaochun Ren, Jiyuan Yang, Zuming Yan, Zihan Wang, Liu Yang, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Xin Xin
Venue: SIGIR
Year: 2025

In sequential recommendation, system exposure refers to items that are exposed to the user. Typically, the user only interactions with a few of the exposed items. Although sequential recommendation has achieved great success in predicting future user interests, existing sequential recommendation methods do not fully exploit system exposure data. Most methods only model items that have been interacted with, while the large volume of exposed but non-interacted items is overlooked. Even methods that consider system exposure typically train the recommender using only the logged historical system exposure, without exploring unseen user interests. In this paper, we propose counterfactual augmentation over system exposure for sequential recommendation (CaseRec). To better model historical system exposure, CaseRec introduces reinforcement learning to account for different exposure rewards. CaseRec uses a decision transformer-based sequential model to take an exposure sequence as input and assigns different rewards according to the user feedback. To explore unseen user interests, CaseRec performs counterfactual augmentation, where exposed items are replaced with counterfactual items. Then, a transformer based user simulator is used to predict the user feedback reward for the augmented items. Augmentation, together with the user simulator, gives rise to counterfactual exposure sequences to uncover new user interests. Finally, CaseRec uses the logged exposure sequences with the counterfactual exposure sequences to train a decision transformer-based sequential model for generating recommendation. Experiments on three real-world benchmarks show the effectiveness of CaseRec. Our code is available at https://github.com/ZiqiZhao1/CaseRec.
Social Relation-Level Privacy Risks and Preservation in Social Recommender Systems
Authors: Xuhao Zhao, Zhongrui Zhang, Yanmin Zhu, Zhaobo Wang, Wenze Ma, Jiadi Yu, Feilong Tang
Venue: SIGIR
Year: 2025

The integration of social information into recommender systems (RSs) has gained significant popularity for enhancing recommendation performance and user experience. However, this practice introduces substantial privacy risks, particularly concerning the leakage of sensitive social relationships. While prior research has primarily focused on user-level and interaction-level privacy risks, the social relation-level privacy risks remain largely unexplored. To fill this gap, we investigate social privacy risks through membership inference attacks (MIA) and propose a Social relation-level MIA (SMIA) framework. Two key challenges arise: (1) the adversary can only access the recommended item IDs, which provide indirect and limited information about social relationships, and (2) extracting socially relevant preferences from recommendation results is inherently difficult. To tackle the first challenge, we leverage shadow models to transform sparse item IDs into dense features, enabling adversaries to effectively utilize recommendation outputs. For the second challenge, SMIA employs a dual-branch learning approach that disentangles social and behavioral preferences. Therefore, we can extract socially relevant signals from the disentangled preferences.Extensive experiments on real-world datasets demonstrate that both social and general RSs are highly vulnerable to such attacks, highlighting the urgent need for robust privacy protection mechanisms. To defend against these attacks, we introduce a Socially Adversarial Learning (SAL) defense mechanism that selectively obscures sensitive social information in user representations during training, effectively reducing privacy leakage. We further evaluate the effectiveness of our defense and discuss future directions for developing privacy-preserving mechanisms in social RSs.
Balancing Self-Presentation and Self-Hiding for Exposure-Aware Recommendation Based on Graph Contrastive Learning
Authors: Leqi Zheng, Chaokun Wang, Ziyang Liu, Canzhi Chen, Cheng Wu, Hongwei Li
Venue: SIGIR
Year: 2025

Recent advances in graph contrastive learning (GCL) have significantly enhanced recommendation systems. However, most existing approaches predominantly focus on optimizing training data fit while overlooking exposure bias, a critical issue that can substantially impact recommendation effectiveness. Drawing inspiration from sociological theories of human interaction patterns-specifically how individuals balance self-presentation and self-hiding behaviors in social contexts-this paper proposes BPH4Rec, a novel Balancing self-Presentation and self-Hiding approach for exposure-aware Recommendation based on GCL. Within the GCL framework, BPH4Rec introduces two complementary mechanisms: (1) a self-hiding mechanism that modifies the adjacency matrix of contrastive views through custom inverse propensity scoring (IPS), effectively addressing exposure bias, and (2) a self-presentation mechanism that incorporates densification factors during matrix reconstruction to mitigate sparsity-induced biases. Through extensive evaluation on six public benchmark datasets, BPH4Rec demonstrates substantial improvements over state-of-the-art baselines, particularly in promoting long-tail item discovery while maintaining recommendation accuracy.
MAAQR: An LLM-based Multi-Agent Framework for Adaptive Query Rewriting in Alipay Search
Authors: Qi Zheng, Mingjie Zhong, Saisai Gong, Huimin Jiang, Kaixin Wu, Hong Liu, Jia Xu, Linjian Mo
Venue: SIGIR
Year: 2025

Query rewriting is essential in e-commerce search, as it bridges the lexical gap between user queries and item descriptions, thereby enhancing search performance. Despite recent advancements, current rewriting approaches are still limited by an inadequate comprehension of domain-specific knowledge and a lack of mechanisms for adaptive refinement in response to new or changing query-item relationships. To overcome these limitations, we propose a large language model (LLM) based Multi-Agent Framework for Adaptive Query Rewriting (MAAQR) in Alipay Search. Initially, we perform knowledge-enhanced fine-tuning to improve the LLM's understanding of query and item semantics. Subsequently, a multi-agent collaborative rewriting architecture is employed to enhance rewrite quality and adaptability. MAAQR has been successfully deployed to serve Alipay's mini-app search since December 2024. Through offline experiments and online A/B testing, MAAQR significantly improves click-through rates (CTR) and the number of transactions for target queries, while substantially reducing the zero-results rate (ZRR).
CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language
Authors: Lin Zhong, Lingzhi Wang, Xu Yang, Qing Liao
Venue: SIGIR
Year: 2025

Large Language Models (LLMs) offer new opportunities for the next Point-Of-Interest (POI) prediction task, leveraging their capabilities in semantic understanding of POI trajectories. However, previous LLM-based methods, which are superficially adapted to next POI prediction, largely overlook critical challenges associated with applying LLMs to this task. Specifically, LLMs encounter two critical challenges: (1) a lack of intrinsic understanding of numeric spatiotemporal data, which hinders accurate modeling of users' spatiotemporal distributions and preferences; and (2) an excessively large and unconstrained candidate POI space, which often results in random or irrelevant predictions. To address these issues, we propose a Collaborative Multi-Agent Framework for Next POI Prediction, named CoMaPOI. Through the close interaction of three specialized agents (Profiler, Forecaster, and Predictor), CoMaPOI collaboratively addresses the two critical challenges. The Profiler agent is responsible for converting numeric data into language descriptions, enhancing semantic understanding. The Forecaster agent focuses on dynamically constraining and refining the candidate POI space. The Predictor agent integrates this information to generate high-precision predictions. Extensive experiments on three benchmark datasets (NYC, TKY, and CA) demonstrate that CoMaPOI achieves state-of-the-art performance, improving all metrics by 5% to 10% compared to SOTA baselines. This work pioneers the investigation of challenges associated with applying LLMs to complex spatiotemporal tasks by leveraging tailored collaborative agents. Our source code is available at: https://github.com/Chips98/CoMaPOI.
Joint Item Embedding Dual-view Exploration and Adaptive Local-Global Fusion for Federated Recommendation
Authors: Pengyang Zhou, Chaochao Chen, Weiming Liu, Wenkai Shen, Xinting Liao, Huarong Deng, Zhihui Fu, Jun Wang, Wu Wen, Xiaolin Zheng
Venue: SIGIR
Year: 2025

Federated Recommendation (FedRec) enables joint training across a large number of clients without centralizing user interaction data. However, existing FedRec methods overlook two key challenges, i.e. (1) sufficiently explore the global item embedding space, and (2) effectively achieve local and global collaboration. The former is caused by client sparsity, which leads to suboptimal item embeddings and subsequently impacts the global item embedding in both the dimension and sample views. The latter arises from the lack of modeling the relative importance of local and global contributions to personalized user preferences. To address the above challenges, we propose FedIAR which contains two modules, i.e., item embedding dual-view exploration and adaptive local-global fusion. The first module enhances the global item embedding by reducing redundancy in the dimension view and capturing latent item relationships in the sample view, improving representational capacity. The second module enables the adaptive fusion of local and global item embeddings based on the user preference representation, achieving personalized optimum for recommendation. Extensive experiments on six datasets demonstrate the effectiveness of FedIAR in improving federated recommendation performance.
Disentangled Graph Debiasing for Next POI Recommendation
Authors: Hailun Zhou, Jiajie Xu, Qiaoming Zhu, Chengfei Liu
Venue: SIGIR
Year: 2025

Graph neural networks play a pivotal role in various location-based applications, showcasing their compelling ability to capture collaborative signals across user check-in sequences. Recent advancements in next POI recommendation have further leveraged spatio-temporal graphs to uncover the transitional and geographical regularities. However, these methods are usually vulnerable due to the presence of data biases in real-life scenarios, which may mislead the model to disproportionately favoring certain POIs. To this end, this paper proposes a new graph debiasing paradigm for POI recommendation, which disentangles causal and bias knowledge within spatio-temporal graphs, allowing for not only the mitigation of bias issues, but also the utilization of causal information from spatial and temporal perspectives. Specifically, to facilitate graph debiasing at its topological level, an adaptive edge mask generator is first designed to explicitly decompose an entangled graph into causal and bias subgraphs. We encourage the stable relationships between the causal subgraph and the prediction, while the bias subgraph targets at the skewed bias distribution. We further enhance the independence between such two parts by employing a causal-bias disagreement regularization to encourage their distribution in separate semantic spaces. In addition, an inter-view contrastive learning module is also applied to maintain the relation discriminability of transitional and geographical representations. Extensive experiments on three real-world datasets demonstrate the superiority of our proposed model on recommendation performance, as well as its robustness against data bias.
μDS: Multi-Objective Data Snippet Extraction for Dataset Search
Authors: Xiao Zhou, Qiaosheng Chen, Jiageng Chen, Gong Cheng
Venue: SIGIR
Year: 2025

With the continuous growth of open data on the Web, dataset search has become a prominent specialized retrieval problem to find datasets relevant to a query. Recent solutions rank datasets based on not only their metadata, but also data snippets extracted from their actual data. While the goodness of a data snippet has been studied from various aspects, in this paper we propose to, for the first time, jointly optimize compactness, relevance, representativeness, and cohesiveness in snippet extraction. To extract such multi-objective data snippets, we formulate a new combinatorial optimization problem and design an efficient algorithm with a proved worst-case approximation ratio. We evaluate the data snippets extracted by our algorithm intrinsically through a set of quality metrics and extrinsically by applying them to dataset search.
OmniNER2025: Diverse and Comprehensive Fine-Grained NER Dataset and Benchmark for Chinese
Authors: Yong Zhou, Shuaipeng Liu, Yunqing Li, Mengting Hu, Wen Dai, Xiaowei Zhao, Xiujuan Xu
Venue: SIGIR
Year: 2025

As Named Entity Recognition (NER) tasks have evolved, artificial intelligence has been widely applied in this field. However, most benchmarks are limited to English, making it challenging to replicate successful experiences in other languages. To expand NER to informal and diverse Chinese text scenarios, we have proposed a new large-scale Chinese NER dataset, OmniNER2025. This dataset, obtained from user posts on a popular Chinese social media platform Xiaohongshu, contains 195,568 samples and 89 categories, all manually annotated. To our knowledge, it is currently the largest Chinese open-source NER dataset in terms of sample size, category diversity, and domain coverage. This dataset is more challenging than existing Chinese NER datasets and better reflects real-world applications. The large sample size and diverse entity types provide valuable research resources. Additionally, we introduced the ERRTA tool for error analysis and teacher model guidance, significantly reducing model errors and improving performance. In the future, we will refine the ERRTA framework and explore optimization strategies to enhance the practical value of NER models. By releasing the OmniNER2025 dataset and introducing the ERRTA tool, we have advanced fine-grained NER research and improved model performance, promoting its application and development in real-world scenarios.
Balancing Precision and Generalization: Dynamic Instruction Generation for Model Adaptive Zero-Shot Reasoning in LLMs
Authors: Ruihan Zhu, Bo Wang, Dongming Zhao, Jing Liu, Ruifang He, Yuexian Hou
Venue: SIGIR
Year: 2025

Current research shows that providing instructions to guide Large Language Models (LLMs) improves reasoning tasks, but existing methods struggle to balance accuracy and generalization. Manually crafted instructions tailored to specific LLMs and tasks improve performance but reduce generalizability, while more general instructions lack detail and lower performance. To address this, we propose a dynamic instruction-generation method using an Instruction-Generation Prompt (IGP). IGP categorizes problems into domains and integrates the model's capabilities to generate detailed task-specific instructions, resulting in a comprehensive plan. This approach achieves high precision with general prompts without requiring in-depth knowledge of LLMs or tasks. We validated our method across five LLMs and ten datasets in three task categories. Our dynamically generated instructions outperformed traditionally handcrafted, LLM-specific instructions across various LLMs and tasks.