OpenRaiser/Intern-Atlas huggingface dataset:
QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking
Authors: Shubham Chatterjee, Jeff Dalton
Venue: SIGIR
Year: 2025

Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representations throughout the ranking process, performing aggregation only at the final scoring stage-an approach we call ''late aggregation.'' We first transform these fine-grained representations through learned attention patterns, then apply carefully chosen mathematical operations for precise matches. Experiments across five standard benchmarks show that QDER achieves significant performance gains, with improvements of 36% in nDCG@20 over the strongest baseline on TREC Robust 2004 and similar improvements on other datasets. QDER particularly excels on difficult queries, achieving an nDCG@20 of 0.70 where traditional approaches fail completely (nDCG@20 = 0.0), setting a foundation for future work in entity-aware retrieval.
U-Sticker: A Large-Scale Multi-Domain User Sticker Dataset for Retrieval and Personalization
Authors: Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo, Weizhi Ma, Qinglang Guo, Min Zhang
Venue: SIGIR
Year: 2025

Instant messaging with texts and stickers has become a widely adopted communication medium, enabling efficient expression of user semantics and emotions. With the increased use of stickers conveying information and feelings, sticker retrieval and recommendation has emerged as an important area of research. However, a major limitation in existing literature has been the lack of datasets capturing temporal and user-specific sticker interactions, which has hindered further progress in user modeling and sticker personalization. To address this, we introduce User-Sticker, a dataset that includes temporal and user anonymous ID across conversations. It is the largest publicly available sticker dataset to date, containing 22K unique users, 370K stickers, and 8.3M messages. The raw data was collected from a popular messaging platform from 67 conversations over 720 hours of crawling. All text and image data were carefully vetted for safety and privacy checks and modifications. Spanning 10 domains, the U-Sticker dataset captures rich temporal, multilingual, and cross-domain behaviors not previously available in other datasets. Extensive quantitative and qualitative experiments demonstrate U-Sticker's practical applications in user behavior modeling and personalized recommendation and highlight its potential to further research areas in personalized retrieval and conversational studies. U-Sticker dataset is publicly available.
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum Inner Product Search
Authors: Tingyang Chen, Cong Fu, Xiangyu Ke, Yunjun Gao, Yabo Ni, Anxiang Zeng
Venue: SIGIR
Year: 2025

Maximum Inner Product Search (MIPS) is a fundamental challenge in machine learning and information retrieval, particularly in high-dimensional data applications. Existing approaches to MIPS either rely solely on Inner Product (IP) similarity, which faces issues with local optima and redundant computations, or reduce the MIPS problem to the Nearest Neighbor Search under the Euclidean metric via space projection, leading to topology destruction and information loss. Despite the divergence of the two paradigms, we argue that there is no inherent binary opposition between IP and Euclidean metrics. By stitching IP and Euclidean in the design of indexing and search algorithms, we can significantly enhance MIPS performance. Specifically, this paper explores the theoretical and empirical connections between these two metrics from the MIPS perspective. Our investigation, grounded in graph-based search, reveals that different indexing and search strategies offer distinct advantages for MIPS, depending on the underlying data topology. Building on these insights, we introduce a novel graph-based index called Metric-Amphibious Graph (MAG) and a corresponding search algorithm, Adaptive Navigation with Metric Switch (ANMS). To facilitate parameter tuning for optimal performance, we identify three statistical indicators that capture essential data topology properties and correlate strongly with parameter tuning. Extensive experiments on 12 real-world datasets demonstrate that MAG outperforms existing state-of-the-art methods, achieving up to 4x search speedup while maintaining adaptability and scalability.
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
Authors: Jinpeng Chen, Jianxiang He, Huan Li, Senzhang Wang, Yuan Cao, Kaimin Wei, Zhenye Yang, Ye Ji
Venue: SIGIR
Year: 2025

Session-based Recommendation (SBR) aims to predict the next item a user will likely engage with, using their interaction sequence within an anonymous session. Existing SBR models often focus only on single-session information, ignoring inter-session relationships and valuable cross-session insights. Some methods try to include inter-session data but struggle with noise and irrelevant information, reducing performance. Additionally, most models rely on item ID co-occurrence and overlook rich semantic details, limiting their ability to capture fine-grained item features. To address these challenges, we propose a novel hierarchical intent-guided optimization approach with pluggable LLM-driven semantic learning for session-based recommendations, called HIPHOP. First, we introduce a pluggable embedding module based on large language models (LLMs) to generate high-quality semantic representations, enhancing item embeddings. Second, HIPHOP utilizes graph neural networks (GNNs) to model item transition relationships and incorporates a dynamic multi-intent capturing module to address users' diverse interests within a session. Additionally, we design a hierarchical inter-session similarity learning module, guided by user intent, to capture global and local session relationships, effectively exploring users' long-term and short-term interests. To mitigate noise, an intent-guided denoising strategy is applied during inter-session learning. Finally, we enhance the model's discriminative capability by using contrastive learning to optimize session representations. Experiments on multiple datasets show that HIPHOP significantly outperforms existing methods, demonstrating its effectiveness in improving recommendation quality. Our code is available: https://github.com/hjx159/HIPHOP.
Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph
Authors: Qiaosheng Chen, Kaijia Huang, Xiao Zhou, Weiqing Luo, Yuanning Cui, Gong Cheng
Venue: SIGIR
Year: 2025

The rapid growth of open source machine learning (ML) resources, such as models and datasets, has accelerated IR research. However, existing platforms like Hugging Face do not explicitly utilize structured representations, limiting advanced queries and analyses such as tracing model evolution and recommending relevant datasets. To fill the gap, we construct HuggingKG, the first large-scale knowledge graph built from the Hugging Face community for ML resource management. With 2.6 million nodes and 6.2 million edges, HuggingKG captures domain-specific relations and rich textual attributes. It enables us to further present HuggingBench, a multi-task benchmark with three novel test collections for IR tasks including resource recommendation, classification, and tracing. Our experiments reveal unique characteristics of HuggingKG and the derived tasks. Both resources are publicly available, expected to advance research in open source resource sharing and management.
Retrieving the Right Law: Enhancing Legal Search with Style Translation
Authors: Szu-Ju Chen, Jing Jin, Sheng-Lun Wei, Chien-Hung Chen, Hsin-Hsi Chen
Venue: SIGIR
Year: 2025

Legal question answering requires accurate retrieval of relevant laws, yet the significant writing style gap between user queries and legal provisions poses a major challenge. Existing datasets and retrieval methods often struggle to capture the complexity of legal language, limiting retrieval effectiveness. In this study, we introduce the Legal Query-to-Provision Retrieval (LQPR) task and construct Query2Provision (Q2P), a dataset designed to enhance law retrieval by incorporating diverse case scenarios and linguistic structures representative of real-world legal inquiries. To address the style disparity, we propose a style translation approach that transforms informal user queries into a more formal legal tone and simplifies complex legal provisions for better alignment. Our experiments demonstrate that integrating writing style transformation significantly improves retrieval performance. The dataset is available at https://github.com/ntunlplab/Query2Provision
ARC: Approximate Relevant Clip Query in Large-Scale Video Repositories
Authors: Yue Chen, Yinan Jing, Ziqiang Yu, Xiaohui Yu, Zhenying He, Kai Zhang, X. Sean Wang
Venue: SIGIR
Year: 2025

The exponential growth of video data highlights the necessity of exploring large-scale video repositories to extract valuable insights. Querying video clips based on content and temporal attributes is a critical task. However, existing solutions face two major challenges: insufficient flexibility in handling complex query conditions involving statistical reasoning and temporal constraints, and low efficiency under high query quality requirements and resource constraints. In this paper, we formally introduce the concept of relevant clip queries for the first time, providing a framework for querying relevant clips in a much more flexible way. To answer such queries efficiently, we propose the approximate query processing system ARC, which aims to maximize the recall and minimize the query overhead while ensuring the confidence of the query results. ARC adopts a proxy model to prune the original video repositories and uses an oracle model to refine the pruned results. Evaluation on real-world video datasets shows that ARC achieves an average speedup of 32.57× compared to naive baselines, while significantly outperforming state-of-the-art methods in almost all performance metrics. Sensitivity and ablation analysis further validate the robustness and effectiveness of the ARC components.
VoRec: Enhancing Recommendation with Voronoi Diagram in Hyperbolic Space
Authors: Yong Chen, Li Li, Wei Peng, Songzhi Su
Venue: SIGIR
Year: 2025

The sparse user-item interactions in recommender systems hinder the quality of embedding representations and degraded recommendation performance. Existing methods attempt to alleviate this sparsity issue by incorporating auxiliary information via item tags, but often neglect structured characteristics of embedding space, such as semantic distribution and logical relations. To this end, we propose VoRec, a novel framework that explores the spatial distribution of items and their associated tags to achieve accurate recommendations in hyperbolic space. Specifically, we employ the Voronoi diagram to partition hyperbolic space into logically related subspaces, based on tag distributions and relationships derived from existing tag taxonomies. In addition, we combine the Voronoi diagram with the Hyperbolic Graph Convolutional Network (HGCN) and exploit the respective advantages of the Poincaré and Lorentz models in hyperbolic space. Finally, we develop two types of Voronoi site update strategies, namely active and passive ones, to optimize the Voronoi diagram for recommendation tasks. The active strategy employs contrastive learning to guide updates to the Voronoi diagram, while the passive strategy adaptively freezes parameters based on information gain to regulate the update rate. Extensive experiments on four real-world benchmark datasets demonstrate that our proposed VoRec framework delivers substantial performance improvements, achieving an average 16.35% enhancement in Recall and NDCG metrics compared to state-of-the-art baselines. The model implementation is publicly available at: https://github.com/s35lay/VoRec.
Class Activation Values: Lucid and Faithful Visual Interpretations for CLIP-based Text-Image Retrievals
Authors: Pengxu Chen, Huazhong Liu, Jihong Ding, Xinghao Huang, Shaojun Zou, Laurence Tianruo Yang
Venue: SIGIR
Year: 2025

Transformer-based text-image matching model, known as CLIP, has garnered significant attention owing to its exceptional performance in text-image retrieval tasks and downstream applications. However, the interpret-ability of CLIP remains underexplored. Existing interpretation methods for Transformers often struggle with incomplete and unreliable attributions within the image and text modalities, respectively. In this paper, we propose a fine-grained interpretation method, termed Class Activation Values (CAV), to provide lucid and faithful visual explanations for CLIP-based text-image retrievals. Specifically, we systematically perform multi-scale accumulation and fusion of class-specific gradients and activation value features to generate high-definition explanations for the image encoder. Furthermore, we present element-wise gradient-based weights to attribute fine-grained relevance between value features and output similarity within the text encoder. The proposed CAV is capable of simultaneously rendering detailed and credible explanations due to its precise feature attribution. Extensive qualitative and quantitative experiments are conducted on the ImageNet-1k and MS COCO datasets, and the experimental results demonstrate that CAV outperforms state-of-the-art interpretation methods in both faithfulness and localization assessments across image and text modalities.
Information Retrieval in Finance: Industry and Academic Perspectives on Innovation
Authors: Chung-Chi Chen, Yongjae Lee, Alejandro Lopez-Lira, Chanyeol Choi, Richard McCreadie, Javier Sanz-Cruzado
Venue: SIGIR
Year: 2025

Information retrieval (IR) plays a critical role in financial decision-making across investment research, trading, risk management, and reporting. With the rise of large language models (LLMs), IR systems have evolved to support more natural, context-aware workflows. In this tutorial, we survey recent advances in applying IR and LLM technologies in finance, covering agent-based simulations, investor recommender systems, retrieval-augmented research management, and LLM-driven portfolio construction. We highlight practical challenges and propose future research directions at the intersection of IR, LLMs, and financial innovation. More materials can be found at http://irfin.nlpfin.com/.
Echoes in the Feed: Evolution-aware Prompt-augmented Micro-video Popularity Prediction
Authors: Wei Chen, Jiao Li, Jian Lang, Zhangtao Cheng, Yong Wang, Fan Zhou
Venue: SIGIR
Year: 2025

Micro-video popularity prediction (MVPP) is a crucial research topic with important implications for social media marketing and stakeholders. Current works in MVPP utilized the pre-trained vision-language models (PVLs) to model the multimodal features for prediction, failing to capture the evolving popularity trend in micro-videos and leading to suboptimal results. To tackle this limitation, we propose EvoPro, an Evolution-aware Prompt-augmented framework that enhances MVPP. First, inspired by the powerful multimodal understanding and text generation skills of Large Multimodal Models (LMMs), an LMM-driven generative retriever is proposed to create contextually rich retrieval queries and perform precise video-to-video retrieval, forming dynamic micro-video support sets that effectively reflect evolving patterns. Building upon this, a graph-based prompter generates evolutionary prompts by capturing the relational structures within the support set. These prompts, representing the latest trend dynamics, serve as few-shot examples to guide PVLs. By integrating evolutionary prompts, the PVLs are empowered to model the evolving popularity trends more accurately, yielding stronger and more predictive representations. Extensive experiments conducted on three benchmarks demonstrate that EvoPro significantly outperforms competitive baselines.
Accelerating Listwise Reranking: Reproducing and Enhancing FIRST
Authors: Zijian Chen, Ronak Pradeep, Jimmy Lin
Venue: SIGIR
Year: 2025

Large language models (LLMs) have emerged as powerful listwise rerankers but remain prohibitively slow for many real-world applications. What's more, training on the language modeling (LM) objective is not intrinsically aligned with reranking tasks. To address these challenges, FIRST, a novel approach for listwise reranking, integrates a learning-to-rank objective and leverages only the logits of the first generated token for reranking, significantly reducing computational overhead while preserving effectiveness. We systematically evaluate the capabilities and limitations of FIRST. By extending its evaluation to TREC Deep Learning collections (DL19-23), we show that FIRST achieves robust out-of-domain effectiveness. Through training FIRST on a variety of backbone models, we demonstrate its generalizability across different model architectures, and achieve effectiveness surpassing the original implementation. Further analysis of the interaction between FIRST and various first-stage retrievers reveals diminishing returns akin to traditional LLM rerankers. A comprehensive latency study confirms that FIRST consistently delivers a 40% efficiency gain over traditional rerankers without sacrificing effectiveness. Notably, while LM training implicitly improves zero-shot single-token reranking, our experiments also highlight potential conflicts between LM pre-training and subsequent fine-tuning on the FIRST objective. These findings pave the way for more efficient and effective listwise reranking in future applications. Our code is available at: https://rankllm.ai.
CoachGPT: A Scaffolding-based Academic Writing Assistant
Authors: Fumian Chen, Sotheara Veng, Joshua Wilson, Xiaoming Li, Hui Fang
Venue: SIGIR
Year: 2025

Academic writing skills are crucial for students' success but can feel overwhelming without proper guidance and practice, particularly when writing in a second language. Traditionally, students ask instructors or search dictionaries, which are not universally accessible. Early writing assistants emerged as rule-based systems that focused on detecting misspellings, subject-verb disagreements, and basic punctuation errors but are inaccurate and lack contextual understanding. Machine learning-based assistants demonstrate a strong ability for language understanding but are expensive to train. Large language models (LLMs) have shown remarkable capabilities in generating responses in natural languages based on given prompts, but they have a fundamental limitation in education: they generate essays without teaching, which can have detrimental effects on learning when misused. To address this limitation, we develop CoachGPT, which leverages LLMs to assist academic writing for those with limited educational resources and those who prefer self-paced learning. CoachGPT is an AI agent-based web application that (1) takes instructions from experienced educators (2) converts instructions into sub-tasks, and (3) provides real-time feedback and suggestions using large language models. This unique scaffolding structure makes CoachGPT unique among existing writing assistants. Compared with existing writing assistants, CoachGPT provides a more immersed writing experience with personalized messages. Our user studies prove the usefulness of CoachGPT and the potential of large language models for academic writing.
Dynamic Margin-based Contrastive Learning for Robust Negative Sampling in Information Retrieval
Authors: Tsai-Tsung Chen, Chuan-Ju Wang, Ming-Feng Tsai
Venue: SIGIR
Year: 2025

Modern information retrieval (IR) systems, powered by bi-encoder architectures and pretrained language models, rely on effective negative sampling for contrastive learning. While easy negatives are computationally simple, they fail to challenge the model, whereas hard negatives-selected via methods like BM25, ANCE, or ADORE-can be overly difficult and misleading. To this end, this paper proposes dynamic margin-based contrastive learning (DMCL), which adaptively adjusts the decision boundary based on query-negative similarity, ensuring consistent exposure to moderately hard negatives. Experiments across diverse datasets and models show that DMCL outperforms traditional methods, achieving state-of-the-art retrieval performance with minimal computational cost.
Collaborative Diffusion Models for Recommendation
Authors: Mengru Chen, Lianghao Xia, Yong Xu, Ronghua Luo
Venue: SIGIR
Year: 2025

Recently, recommendation models based on collaborative filtering have increasingly leveraged not only primary user-item interactions but also auxiliary information such as implicit relational structures (e.g., user-user or item-item graphs) and multimodal content (e.g., images and textual descriptions) to enhance recommendation performance. A key challenge in this context lies in effectively integrating auxiliary features derived from semantic structures or modality representations into user-item modeling, in a way that enhance performance without incurring detrimental effects. Furthermore, since these features often originate from heterogeneous semantic or modal spaces, they may include redundant or task-irrelevant information that can hinder the learning process. To address these issues, we propose the Collaborative Diffusion Models for Recommendation (CoDMR). CoDMR employs diffusion models in latent feature spaces to filter out task-irrelevant noise embedded in auxiliary features. It introduces task-relevant collaborative signals as conditional guidance during the denoising process, facilitating the generation of auxiliary representations aligned with the recommendation task. These refined features are then incorporated into user-item interaction modeling, resulting in enhanced representations for both users and items. Extensive experiments on three public datasets consistently demonstrate that our CoDMR method outperforms various competitive baselines. The source code of the model implementation is available at the link https://github.com/cmr123456/CoDMR.