OpenRaiser/Intern-Atlas huggingface dataset:
Multi-level Encoding with Hierarchical Alignment for Sketch-Based 3D Shape Retrieval
Authors: Donglin Zhang, Changxing Li, Xiao-Jun Wu
Venue: SIGIR
Year: 2025

Sketch-based 3D shape retrieval (SBSR) aims to retrieve 3D shapes using hand-drawn sketches as query inputs. Although existing SBSR methods have achieved promising results, several challenges still require further investigation. First, most existing approaches usually leverage simple aggregation schemes, often failing to capture the intrinsic relationships between views, which limits the effectiveness of 3D shape feature extraction. Second, conventional SBSR primarily focuses on instance-level alignment while ignoring multi-level alignment, which may neglect complex hierarchical relationships. To address these limitations, we propose a novel Multi-level Encoding with Hierarchical Alignment (MEHA) method for SBSR. Specifically, we adopt spatial encoding and view encoding for multiple views of 3D shapes. The proposed aggregation scheme then integrates these multi-level embedded local features to enhance the representation of 3D shape features. Considering the complexity of 3D shapes, MEHA adopts a two-stage training process: the first stage focuses on learning 3D shape features, while the second stage emphasizes modality alignment. Furthermore, we introduce a hierarchical alignment strategy that bridges the modality gap through instance-level, prototype-level, and centre-level alignment. Extensive experiments on two public benchmark datasets demonstrate the superiority of our method, showing that MEHA outperforms the state-of-the-art baselines.
SAGraph: A Large-Scale Social Graph Dataset with Comprehensive Context for Influencer Selection in Marketing
Authors: Xiaoqing Zhang, Yuhan Liu, Jianzhou Wang, Zhenxing Hu, Xiuying Chen, Rui Yan
Venue: SIGIR
Year: 2025

Influencer marketing campaign success heavily depends on identifying key opinion leaders who can effectively leverage their credibility and reach to promote products or services. The selection of influencers is vital for boosting brand visibility, fostering consumer trust, and driving sales. While traditional research often simplifies complex factors like user attitudes, interaction frequency, and advertising content, into simple numerical values. However, this reductionist approach fails to capture the dynamic nature of influencer marketing effectiveness. To bridge this gap, we present SAGraph, a novel comprehensive dataset from Weibo that captures multi-dimensional marketing campaign data across six product domains. The dataset encompasses 345,039 user profiles with their complete interaction histories, including 1.3M comments and 554K reposts across 44K posts, providing unprecedented granularity in influencer marketing dynamics. SAGraph uniquely integrates user profiles, content features, and temporal interaction patterns, enabling in-depth analysis of influencer marketing mechanisms. Experimental results using both traditional baselines and state-of-the-art large language models (LLMs) demonstrate the crucial role of content analysis in predicting advertising effectiveness. Our findings reveal that LLM-based approaches achieve superior performance in understanding and predicting campaign success, opening new avenues for data-driven influencer marketing strategies. We hope that this dataset will inspire further research: https://github.com/xiaoqzhwhu/SAGraph/.
Replication and Exploration of Generative Retrieval over Dynamic Corpora
Authors: Zhen Zhang, Xinyu Ma, Weiwei Sun, Pengjie Ren, Zhumin Chen, Shuaiqiang Wang, Dawei Yin, Maarten de Rijke, Zhaochun Ren
Venue: SIGIR
Year: 2025

Generative retrieval (GR) has emerged as a promising paradigm in information retrieval (IR). However, most existing GR models are developed and evaluated using a static document collection, and their performance in dynamic corpora where document collections evolve continuously is rarely studied. In this paper, we first reproduce and systematically evaluate various representative GR approaches over dynamic corpora. Through extensive experiments, we reveal that existing GR models with text-based docids show superior generalization to unseen documents. We observe that the more fine-grained the docid design in the GR model, the better its performance over dynamic corpora, surpassing BM25 and even being comparable to dense retrieval methods. While GR models with numeric-based docids show high efficiency, their performance drops significantly over dynamic corpora. Furthermore, our experiments find that the underperformance of numeric-based docids is partly due to their excessive tendency toward the initial document set, which likely results from overfitting on the training set. We then conduct an in-depth analysis of the best-performing GR methods. We identify three critical advantages of text-based docids in dynamic corpora: (i) Semantic alignment with language models' pretrained knowledge (ii) Fine-grained docid design, and (iii) High lexical diversity. Building on these insights, we finally propose a novel multi-docid design that leverages both the efficiency of numeric-based docids and the effectiveness of text-based docids, achieving improved performance in dynamic corpus without requiring additional retraining. Our work offers empirical evidence for advancing GR methods over dynamic corpora and paves the way for developing more generalized yet efficient GR models in real-world search engines.
Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model
Authors: Luankang Zhang, Kenan Song, Yi Quan Lee, Wei Guo, Hao Wang, Yawen Li, Huifeng Guo, Yong Liu, Defu Lian, Enhong Chen
Venue: SIGIR
Year: 2025

In recommendation systems, the traditional multi-stage paradigm, which includes retrieval and ranking, often suffers from information loss between stages and diminishes performance. Recent advances in generative models, inspired by natural language processing, suggest the potential for unifying these stages to mitigate such loss. This paper presents the Unified Generative Recommendation Framework (UniGRF), a novel approach that integrates retrieval and ranking into a single generative model. By treating both stages as sequence generation tasks, UniGRF enables sufficient information sharing without additional computational costs, while remaining model-agnostic. To enhance inter-stage collaboration, UniGRF introduces a ranking-driven enhancer module that leverages the precision of the ranking stage to refine retrieval processes, creating an enhancement loop. Besides, a gradient-guided adaptive weighter is incorporated to dynamically balance the optimization of retrieval and ranking, ensuring synchronized performance improvements. Extensive experiments demonstrate that UniGRF significantly outperforms existing models on benchmark datasets, confirming its effectiveness in facilitating information transfer. Ablation studies and further experiments reveal that UniGRF not only promotes efficient collaboration between stages but also achieves synchronized optimization. UniGRF provides an effective, scalable, and compatible framework for generative recommendation systems.
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass
Authors: Bowen Zhang, Zixin Song, Chunping Li
Venue: SIGIR
Year: 2025

As a fundamental task in Information Retrieval and Computational Linguistics, sentence representation has profound implications for a wide range of practical applications such as text clustering, content analysis, question-answering systems, and web search. Recent advances in pre-trained language models (PLMs) have driven remarkable progress in this field, particularly through unsupervised embedding derivation methods centered on discriminative PLMs like BERT. However, due to time and computational constraints, few efforts have attempted to integrate unsupervised sentence representation with generative PLMs, which typically possess much larger parameter sizes. Given that state-of-the-art models in both academia and industry are predominantly based on generative architectures, there is a pressing need for an efficient unsupervised text representation framework tailored to decoder-only PLMs. To address this concern, we propose CSE-SFP, an innovative method that exploits the structural characteristics of generative models. Compared to existing strategies, CSE-SFP requires only a single forward pass to perform effective unsupervised contrastive learning. Rigorous experimentation demonstrates that CSE-SFP not only produces higher-quality embeddings but also significantly reduces both training time and memory consumption. Furthermore, we introduce two ratio metrics that jointly assess alignment and uniformity, thereby providing a more robust means for evaluating the semantic spatial properties of encoding models. Our code and checkpoints are available at https://github.com/ZBWpro/CSE-SFP.
Fair Recommendation with Biased-Limited Sensitive Attribute
Authors: Jizhi Zhang, Haoyu Shen, Tianhao Shi, Keqin Bao, Xin Chen, Yang Zhang, Fuli Feng
Venue: SIGIR
Year: 2025

Ensuring fair recommendations for users with different sensitive attributes is essential for building trustworthy recommender systems. A significant challenge in achieving this in the real world is that some users are unwilling to disclose their sensitive attributes, limiting the applicability of traditional approaches. Recent efforts have attempted to address this challenge by reconstructing sensitive attributes based on the observed data. However, the observed data often does not represent an unbiased sample of the true distribution, rendering the reconstructed results unreliable. Moreover, it is difficult to select a debiasing method to achieve unbiased reconstruction, due to lacking sufficient prior knowledge about the bias. This motivates us to develop new fairness approaches. This work proposes a new method, Multiple Prior-Guided Robust Optimization (MPR), to achieve fair recommendations under biased observations of sensitive attributes, without requiring real sensitive attribute distribution. MPR begins by estimating various potential distributions of sensitive attributes using multiple randomly set priors, and then ensures fairness by minimizing the worst-case unfairness across all estimations. Thus, MPR can ensure fairness optimization under the real distribution approximately once one of the estimates approaches the true distribution. Both theoretical and empirical evidence demonstrate that MPR effectively ensures fairness in recommender systems when sensitive attribute observations are limited and biased. The code and data are released at https://github.com/jizhi-zhang/MPR.
Negative Exclusion Filtering: Optimizing Ad Delivery Efficiency for Large-Scale Social Media Platforms
Authors: Min Zhang, Ganlin Song, Fang Zhou, Dong Liang, Jianwei Xiao, Shengyu Huang, Lizhang Qin, Xi Yan, Rong Shi, Xiyuan Chen, Jing Xu, Zhaojun Zhang, Ye Wang, Gautam Srinivasan, Yang Wang, Qianru Li, Mahesh Masale, Zhengyu Zhang, Zeliang Chen, Ellie Wen, Puneet Sharma
Venue: SIGIR
Year: 2025

The volume of ads ranked impacts the performance of ad ranking systems. To enhance efficiency, multi-stage ranking systems are widely studied in academia and adopted across industry. However, as large-scale deep learning recommendation models gain prevalence, resource constraints-especially CPU and GPU limitations-have become a significant bottleneck. These constraints can hinder model iteration and lead to incomplete ranking, causing regressions in user experience and ad performance. To address these issues, we analyzed ad ranking metrics and found that ad rankings for individual users remain relatively stable over short periods. Based on this insight, we introduce Negative Exclusion Filtering, a framework that optimizes the balance between ranked ad volume and computing resources. By skipping re-ranking of consistently low-ranked ads for each user request, it reduces computing cost in large-scale social media environments. Negative Exclusion Filtering has achieved CPU reductions of 3.7-7.4% and GPU reductions of 4.0-7.9% in power consumption across various Meta products, along with a 0.5-2.0% reduction in latency, all while maintaining recommendation quality and performance.
Exploring Human-Like Thinking in Search Simulations with Large Language Models
Authors: Erhan Zhang, Xingzhu Wang, Peiyuan Gong, Zixuan Yang, Jiaxin Mao
Venue: SIGIR
Year: 2025

Simulating user search behavior is a critical task in information retrieval, which can be employed for user behavior modeling, data augmentation, and system evaluation. Recent advancements in large language models (LLMs) have opened up new possibilities for generating human-like actions including querying, browsing, and clicking. In this work, we explore the integration of human-like thinking into search simulations by leveraging LLMs to simulate users' hidden cognitive processes. Specifically, given a search task and context, we prompt LLMs to first think like a human before executing the corresponding action. As existing search datasets do not include users' thought processes, we conducted a user study to collect a new dataset enriched with users' explicit thinking. We investigate the impact of incorporating such human-like thinking on simulation performance and apply supervised fine-tuning (SFT) to teach LLMs to emulate both human thinking and actions. Our experiments span two dimensions in leveraging LLMs for user simulation: (1) with or without explicit thinking, and (2) with or without fine-tuning on the thinking-augmented dataset. The results demonstrate the feasibility and potential of incorporating human-like thinking in user simulations, though performance improvements on some metrics remain modest. We believe this exploration provides new avenues and inspirations for advancing user behavior modeling in search simulations.
Ask and Retrieve Knowledge: Towards Proactive Asking with Imperfect Information in Medical Multi-turn Dialogues
Authors: Bolin Zhang, Shengwei Wang, Yangqin Jiang, Dianbo Sui, Zhiying Tu, Dianhui Chu
Venue: SIGIR
Year: 2025

Large language models (LLMs) cannot effectively collaborate with humans who provide imperfect information at the initial stage of the dialogue, unless they learn to proactively ask questions. Our core idea is to enable LLMs to decide whether to take the action of ''ask'' or ''tell'' at each turn by self-reasoning, with the belief of the decisions enhanced by retrieving knowledge related to the user input. Thus, we propose the ask and retrieve knowledge framework (Ark), where LLMs think through what to retrieve, when to stop retrieving, and then take actions accordingly. Ark is used to produce the action paths for model training. To mitigate the collapse of models trained on synthetic data, we propose a progressive training strategy: self-reason learning by supervised fine-tuning on produced paths and knowledge alignment through direct preference optimization on doctor response. To evaluate the information gain brought by the ask action, we design a method to calculate the ask utility value (AUV) based on the expected value of perfect information (EVPI) theory. Although MedArk is trained using synthetic data from GPT-4o-mini, it highly outperforms GPT-4o and other medical LLMs in six aspects: helpfulness, hallucination, action selection, BERTScore, AUV, and asking correctness. MedArk also achieves SOTA results in the perfect information scenario, i.e., medical examinations. We release our code, data and models at https://github.com/Bolin97/MedArk.
Highly Efficient Disk-based Nearest Neighbor Search on Extended Neighborhood Graph
Authors: Cheng Zhang, Jianzhi Wang, Wan-Lei Zhao, Shihai Xiao
Venue: SIGIR
Year: 2025

Nearest neighbor search (NN search) plays a fundamental role in many disciplines. According to recent studies, graph-based search methods show superior performance over other types of methods. In order to accommodate the high dimensionality as well as the growing data-scale, the disk-based NN search in which the index graph and the full-precision vectors are kept in SSD has become a promising direction. This paper optimizes the disk-based NN search from three perspectives. Firstly, an eXtended Neighborhood Graph (XN-Graph) structure is proposed. In contrast to the existing index graphs, the out-edges of the graph neighborhood are collected from much wider coverage of the data space. It therefore reduces the number of hops during NN search, which in turn reduces the search latency. Additionally, a dataset partitioning method called Boundary-adaptive Balanced Partition is proposed to facilitate the graph construction in cases where the system cannot handle large datasets in a single round. Moreover, an efficient hybrid NN search method called In-Memory First Search is proposed. Compared to the existing methods, it considerably reduces the CPU idle times. With the support of XN-Graph, it shows 1.5-3 times lower search latency than SOTA methods. On billion-scale datasets, its QPS is still above 4000 when Recall@10 is as high as 0.9.
UPPR+: Scaling Uncertain Personalised PageRank Computation on Billion-Sized Graphs with Mutually Exclusive Edges
Authors: Min Zhang, Weiren Yu
Venue: SIGIR
Year: 2025

While Personalised PageRank (PPR) is widely used for ranking nodes in certain graphs, research on PPR for uncertain graphs remains limited. Real-world graphs often exhibit uncertainty in some edges with interdependent probabilities. The best-of-breed work by Kim et al.[13] proposed a fast approximate algorithm, UPPR, leveraging the Sherman-Morrison formula with singular value decomposition. However, UPPR lacks error guarantees, and struggles to scale on large graphs due to the high cost to precompute block matrix inverses over the certain part of the graph. To address these problems, we propose UPPR+, an efficient scheme to retrieve PPR on billion-scale uncertain graphs. 1) We first propose an efficient approach that groupifies uncertain edges to remove duplicates of source node set and maps uncertainties into a small subspace using low-dimensional embeddings. 2) To further accelerate the computation, we devise a novel uncertain subspace reduction method and advance the aggregation of PPRs over all possible worlds within the subspace via ''subset embedding''. 3) We provide theoretical guarantees on the exactness of UPPR+ and analyse its time and space complexities. Extensive empirical studies on various real datasets validate that UPPR+ is 23-106x faster than state-of-the-art competitors, and scales well on billion-edge graphs without compromising accuracy.
Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs
Authors: Taiyan Zhang, Renchi Yang, Yurui Lai, Mingyu Yan, Xiaochun Ye, Dongrui Fan
Venue: SIGIR
Year: 2025

Graph neural networks (GNNs) have become the preferred models for node classification in graph data due to their robust capabilities in integrating graph structures and attributes. However, these models heavily depend on a substantial amount of high-quality labeled data for training, which is often costly to obtain. With the rise of large language models (LLMs), a promising approach is to utilize their exceptional zero-shot capabilities and extensive knowledge for node labeling. Despite encouraging results, this approach either requires numerous queries to LLMs or suffers from reduced performance due to noisy labels generated by LLMs. To address these challenges, we introduce Locle, an active self-training framework that does Label-free nOde Classification with LLMs cost-Effectively. Locle iteratively identifies small sets of ''critical'' samples using GNNs and extracts informative pseudo-labels for them with both LLMs and GNNs, serving as additional supervision signals to enhance model training. Specifically, Locle comprises three key components: (i) an effective active node selection strategy for initial annotations; (ii) a careful sample selection scheme to identify ''critical'' nodes based on label disharmonicity and entropy; and (iii) a label refinement module that combines LLMs and GNNs with a rewired topology. Extensive experiments on five benchmark text-attributed graph datasets demonstrate that Locle significantly outperforms state-of-the-art methods under the same query budget to LLMs in terms of label-free node classification. Notably, on the DBLP dataset with 14.3k nodes, Locle achieves an 8.08% improvement in accuracy over the state-of-the-art at a cost of less than one cent. Our code is available at https://github.com/HKBU-LAGAS/Locle.
EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition
Authors: Jian Zhang, Tianqing Zhang, Qi Li, Hongwei Wang
Venue: SIGIR
Year: 2025

Nested NER tasks have some challenges in specific domains, such as biomedical and industrial fields, particularly due to low resource and class imbalance, which impede its wide application. In this study, we design a novel loss EIoU-EMC, by enhancing the implement of Intersection over Union loss and Multi-class loss. Our proposed method specially leverages the information of entity boundary and entity classification, thereby enhancing the model's capacity to learn from a limited number of data samples. To validate the performance of this innovative method in enhancing NER task, we conducted experiments on three distinct biomedical NER datasets and one dataset constructed by ourselves from industrial complex equipment maintenance documents. Comparing to strong baselines, our method demonstrates the competitive performance across all datasets. During the experimental analysis, our proposed method exhibits significant advancements in entity boundary recognition and entity classification. Our code and data are available at https://github.com/luminous11/EIoU-EMC/
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
Authors: Yu Zhang, Yiwen Zhang, Yi Zhang, Lei Sang, Yun Yang
Venue: SIGIR
Year: 2025

Personalized recommendation is widely used in the web applications, and graph contrastive learning (GCL) has gradually become a dominant approach in recommender systems, primarily due to its ability to extract self-supervised signals from raw interaction data, effectively alleviating the problem of data sparsity. A classic GCL-based method typically uses data augmentation during graph convolution to generates more contrastive views, and performs contrast on these new views to obtain rich self-supervised signals. Despite this paradigm is effective, the reasons behind the performance gains remain a mystery. In this paper, we first reveal via theoretical derivation that the gradient descent process of the CL objective is formally equivalent to graph convolution, which implies that CL objective inherently supports neighborhood aggregation on interaction graphs. We further substantiate this capability through experimental validation and identify common misconceptions in the selection of positive samples in previous methods, which limit the potential of CL objective. Based on this discovery, we propose the Light Contrastive Collaborative Filtering (LightCCF) method, which introduces a novel neighborhood aggregation objective to bring users closer to all interacted items while pushing them away from other positive pairs, thus achieving high-quality neighborhood aggregation with very low time complexity. On three highly sparse public datasets, the proposed method effectively aggregate neighborhood information while preventing graph over-smoothing, demonstrating significant improvements over existing GCL-based counterparts in both training efficiency and recommendation accuracy. Our implementations are publicly accessible.