OpenRaiser/Intern-Atlas huggingface dataset:
CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Authors: Junze Chen, Xinjie Yang, Cheng Yang, Junfei Bao, Zeyuan Guo, Yawen Li, Chuan Shi
Venue: SIGIR
Year: 2025

Recommender systems (RSs) are designed to retrieve candidate items a user might be interested in from a large pool, with a typical approach being the use of graph neural networks (GNNs) to capture high-order interaction relationships. As large language models (LLMs) have demonstrated remarkable success across various domains, researchers are exploring ways to apply their capabilities for improving recommendation performance. However, existing work limits the use of LLMs to either re-ranking recommendation results of traditional RSs or pre-processing the datasets as data augmenters. Both lines of work failed to explore LLMs' capabilities during the filtering process of candidate items, which may lead to suboptimal performance. Instead, we propose to leverage LLMs' reasoning abilities during the candidate filtering process, and introduce Chain Of Retrieval ON grAphs (CORONA) to progressively narrow down the range of candidate items on interaction graphs with the help of LLMs: (1) First, LLM performs preference reasoning based on user profiles, with the response serving as a query to extract relevant users and items from the interaction graph as preference-assisted retrieval ; (2) Then, using the information retrieved in the previous step along with the purchase history of target user, LLM conducts intent reasoning to help refine an even smaller interaction subgraph as intent-assisted retrieval ; (3) Finally, we employ a GNN to capture high-order collaborative filtering information from the extracted subgraph, performing GNN-enhanced retrieval to generate the final recommendation results. The proposed framework leverages the reasoning capabilities of LLMs during the retrieval process, while seamlessly integrating GNNs to enhance overall recommendation performance. Extensive experiments on various datasets and settings demonstrate that our proposed CORONA achieves state-of-the-art (SOTA) performance with an 18.6% relative improvement in recall and an 18.4% relative improvement in NDCG on average. Our code is available on GitHub at https://github.com/BUPT-GAMMA/CORONA.
Large Language Models Enhanced Hyperbolic Space Recommender Systems
Authors: Wentao Cheng, Zhida Qin, Zexue Wu, Pengzhan Zhou, Tianyu Huang
Venue: SIGIR
Year: 2025

Large Language Models (LLMs) have attracted significant attention in recommender systems for their excellent world knowledge capabilities. However, existing methods that rely on Euclidean space struggle to capture the rich hierarchical information inherent in textual and semantic data, which is essential for capturing user preferences. The geometric properties of hyperbolic space offer a promising solution to address this issue. Nevertheless, integrating LLMs-based methods with hyperbolic space to effectively extract and incorporate diverse hierarchical information is non-trivial. To this end, we propose a model-agnostic framework, named HyperLLM, which extracts and integrates hierarchical information from both structural and semantic perspectives. Structurally, HyperLLM uses LLMs to generate multi-level classification tags with hierarchical parent-child relationships for each item. Then, tag-item and user-item interactions are jointly learned and aligned through contrastive learning, thereby providing the model with clear hierarchical information. Semantically, HyperLLM introduces a novel meta-optimized strategy to extract hierarchical information from semantic embeddings and bridge the gap between the semantic and collaborative spaces for seamless integration. Extensive experiments show that HyperLLM significantly outperforms recommender systems based on hyperbolic space and LLMs, achieving performance improvements of over 40%. Furthermore, HyperLLM not only improves recommender performance but also enhances training stability, highlighting the critical role of hierarchical information in recommender systems.
NR4DER: Neural Re-ranking for Diversified Exercise Recommendation
Authors: Xinghe Cheng, Xufang Zhou, Liangda Fang, Chaobo He, Yuyu Zhou, Weiqi Luo, Zhiguo Gong, Quanlong Guan
Venue: SIGIR
Year: 2025

With the widespread adoption of online education platforms, an increasing number of students are gaining new knowledge through Massive Open Online Courses (MOOCs). Exercise recommendation have made strides toward improving student learning outcomes. However, existing methods not only struggle with high dropout rates but also fail to match the diverse learning pace of students. They frequently face difficulties in adjusting to inactive students' learning patterns and in accommodating individualized learning paces, resulting in limited accuracy and diversity in recommendations. To tackle these challenges, we propose Neural Re-ranking for Diversified Exercise Recommendation (in short, NR4DER). NR4DER first leverages the mLSTM model to improve the effectiveness of the exercise filter module. It then employs a sequence enhancement method to enhance the representation of inactive students, accurately matches students with exercises of appropriate difficulty. Finally, it utilizes neural re-ranking to generate diverse recommendation lists based on individual students' learning histories. Extensive experimental results indicate that NR4DER significantly outperforms existing methods across multiple real-world datasets and effectively caters to the diverse learning pace of students.
Dynamic Time-aware Continual User Representation Learning
Authors: Seungyoon Choi, Sein Kim, Hongseok Kang, Wonjoong Kim, Chanyoung Park
Venue: SIGIR
Year: 2025

Traditional user modeling (UM) approaches have primarily focused on designing models for a single specific task, but they face limitations in generalization and adaptability across various tasks. Recognizing these challenges, recent studies have shifted towards continual learning (CL)-based universal user representation learning aiming to develop a single model capable of handling multiple tasks. Despite advancements, existing methods are in fact evaluated under an unrealistic scenario that does not consider the passage of time as tasks progress, which overlooks newly emerged items that may change the item distribution of previous tasks. In this paper, we introduce a practical evaluation scenario on which CL-based universal user representation learning approaches should be evaluated, which takes into account the passage of time as tasks progress. Then, we propose a novel framework DynamIc Time-aware conTinual user representatiOn learner, named DITTO, designed to alleviate catastrophic forgetting despite continuous shifts in item distribution, while also allowing the knowledge acquired from previous tasks to adapt to the current shifted item distribution. Through our extensive experiments, we demonstrate the superiority of DITTO over state-of-the-art methods under a practical evaluation scenario. Our source code is available at https://github.com/seungyoon-Choi/DITTO_official.
ClusterChat: Multi-Feature Search for Corpus Exploration
Authors: Ashish Chouhan, Saifeldin Mandour, Michael Gertz
Venue: SIGIR
Year: 2025

Exploring large-scale text corpora presents a significant challenge in biomedical, finance, and legal domains, where vast amounts of documents are continuously published. Traditional search methods, such as keyword-based search, often retrieve documents in isolation, limiting the user's ability to easily inspect corpus-wide trends and relationships. We present ClusterChat. The demo video and source code are available at: https://github.com/achouhan93/ClusterChat, an open-source system for corpus exploration that integrates cluster-based organization of documents using textual embeddings with lexical and semantic search, timeline-driven exploration, and corpus and document-level question answering (QA) as multi-feature search capabilities. We validate the system with two case studies on a four million abstract PubMed dataset, demonstrating that ClusterChat enhances corpus exploration by delivering context-aware insights while maintaining scalability and responsiveness on large-scale document collections.
Locality-Sensitive Indexing for Graph-Based Approximate Nearest Neighbor Search
Authors: Jun Woo Chung, Huawei Lin, Weijie Zhao
Venue: SIGIR
Year: 2025

The burgeoning size of modern text datasets has heightened the need for efficient text retrieval systems. For such applications, Approximate Nearest Neighbor (ANN) search algorithms, and in particular graph-based methods have long been established as the leading approach in terms of recall and search speed. However, the data and execution dependencies of vertices increase the construction workload and complicate maintenance processes for the constructed index. In this paper, we present Locality-Sensitive Indexing for Graph-Based Search (or LIGS), which utilizes independent locality-sensitive hashing algorithms to simulate a proximity graph, on which a standard graph search can be performed. We show that LIGS offers substantially faster maintenance (insertion/deletion) speeds and better conservation of graph quality compared to state-of-the-art graph-based ANN methods, demonstrating LIGS as a promising alternative for maintenance-heavy scenarios.
Aligning Web Query Generation with Ranking Objectives via Direct Preference Optimization
Authors: João Coelho, Bruno Martins, João Magalhães, Chenyan Xiong
Venue: SIGIR
Year: 2025

Neural retrieval models excel in Web search, but their training requires substantial amounts of labeled query-document pairs, which are costly to obtain. With the widespread availability of Web document collections like ClueWeb22, synthetic queries generated by large language models offer a scalable alternative. Still, synthetic training queries often vary in quality, which leads to suboptimal downstream retrieval performance. Existing methods typically filter out noisy query-document pairs based on signals from an external re-ranker. In contrast, we propose a framework that leverages Direct Preference Optimization (DPO) to integrate ranking signals into the query generation process, aiming to directly optimize the model towards generating high-quality queries that maximize downstream retrieval effectiveness. Experiments show higher ranker-assessed relevance between query-document pairs after DPO, leading to stronger downstream performance on the MS~MARCO benchmark when compared to baseline models trained with synthetic data.
nlcTables: A Dataset for Marrying Natural Language Conditions with Table Discovery
Authors: Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, Gang Chen
Venue: SIGIR
Year: 2025

With the growing abundance of repositories containing tabular data, discovering relevant tables for in-depth analysis remains a challenging task. Existing table discovery methods primarily retrieve desired tables based on a query table or several vague keywords, leaving users to manually filter large result sets. To address this limitation, we propose a new task: NL-conditional table discovery (nlcTD), where users combine a query table with natural language (NL) requirements to refine search results. To advance research in this area, we present nlcTables, a comprehensive benchmark dataset comprising 627 diverse queries spanning NL-only, union, join, and fuzzy conditions, 22,080 candidate tables, and 21,200 relevance annotations. Our evaluation of six state-of-the-art table discovery methods on nlcTables reveals substantial performance gaps, highlighting the need for advanced techniques to tackle this challenging nlcTD scenario. The dataset, construction framework, and baseline implementations are publicly available at https://github.com/SuDIS-ZJU/nlcTables to foster future research.
CIRAG: Retrieval-Augmented Language Model with Collective Intelligence
Authors: Chenxu Cui, Haihui Fan, Jinchao Zhang, Lin Shen, Bo Li, Weiping Wang
Venue: SIGIR
Year: 2025

Retrieval-augmented generation (RAG) paradigms can integrate external knowledge to enhance and validate the output of Large Language Models (LLMs) thereby mitigating generative hallucinations and broadening the model's knowledge scope. Despite advancements, existing RAG methods still suffer from uncertainty of prediction during the multi-round retrieval-generation process, and a lack of the ability to balance the adequacy and redundancy of retrieved information. To address these challenges, we propose CIRAG, an approach that combines the RAG process with collective intelligence. Inspired by the crowd of wisdom, CIRAG simulates individual independent decision-making and information aggregation within a crowd. Specifically, CIRAG first enhances retrieval diversity by expanding queries based on extracted entities, then combines frequency-based and semantic-based reranking to form a multi granularity fusion reranking thereby assessing better relevance, and integrate multiple information sources for accurate content generation. By undertaking these steps in an integrated manner, CIRAG enables the model to acquire comprehensive and non-redundant information for generating responses. We conduct extensive experiments with HotPotQA and 2WikiMultihopQA datasets, popular benchmark for retrieval-based, multi-step question-answering. Experimental results show that our approach surpasses existing advanced RAG framework while providing high portability in query expansion as well as strong comprehensiveness exhibited in the collective intelligence.
Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction
Authors: Xiaoxi Cui, Weihai Lu, Yu Tong, Yiheng Li, Zhejun Zhao
Venue: SIGIR
Year: 2025

In click-through rate prediction, click-through rate prediction is used to model users' interests. However, most of the existing CTR prediction methods are mainly based on the ID modality. As a result, they are unable to comprehensively model users' multi-modal preferences. Therefore, it is necessary to introduce multi-modal CTR prediction. Although it seems appealing to directly apply the existing multi-modal fusion methods to click-through rate prediction models, these methods (1) fail to effectively disentangle commonalities and specificities across different modalities; (2) fail to consider the synergistic effects between modalities and model the complex interactions between modalities. To address the above issues, this paper proposes the Diffusion-based Multi-modal Synergy Interest Network (Diff-MSIN) framework for click-through prediction. This framework introduces three innovative modules: the Multi-modal Feature Enhancement (MFE) Module Synergistic Relationship Capture (SRC) Module, and the Feature Dynamic Adaptive Fusion (FDAF) Module. The MFE Module and SRC Module extract synergistic, common, and special information among different modalities. They effectively enhances the representation of the modalities, improving the overall quality of the fusion. To encourage distinctiveness among different features, we design a Knowledge Decoupling method. Additionally, the FDAF Module focuses on capturing user preferences and reducing fusion noise. To validate the effectiveness of the Diff-MSIN framework, we conducted extensive experiments using the Rec-Tmall and three Amazon datasets. The results demonstrate that our approach yields a significant improvement of at least 1.67% compared to the baseline, highlighting its potential for enhancing multi-modal recommendation systems. Our code is available at the following link: https://github.com/Cxx-0/Diff-MSIN.
Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
Authors: Xiaoxi Cui, Weihai Lu, Yu Tong, Yiheng Li, Zhejun Zhao
Venue: SIGIR
Year: 2025

The sequential recommendation system utilizes historical user interactions to predict preferences. Effectively integrating diverse user behavior patterns with rich multimodal information of items to enhance the accuracy of sequential recommendations is an emerging and challenging research direction. This paper focuses on the problem of multi-modal multi-behavior sequential recommendation, aiming to address the following challenges: (1) the lack of effective characterization of modal preferences across different behaviors, as user attention to different item modalities varies depending on the behavior; (2) the difficulty of effectively mitigating implicit noise in user behavior, such as unintended actions like accidental clicks; (3) the inability to handle modality noise in multi-modal representations, which further impacts the accurate modeling of user preferences. To tackle these issues, we propose a novel Multi-Modal Multi-Behavior Sequential Recommendation model (M3BSR). This model first removes noise in multi-modal representations using a Conditional Diffusion Modality Denoising Layer. Subsequently, it utilizes deep behavioral information to guide the denoising of shallow behavioral data, thereby alleviating the impact of noise in implicit feedback through Conditional Diffusion Behavior Denoising. Finally, by introducing a Multi-Expert Interest Extraction Layer, M3BSR explicitly models the common and specific interests across behaviors and modalities to enhance recommendation performance. Experimental results indicate that M3BSR significantly outperforms existing state-of-the-art methods on benchmark datasets.
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems
Authors: Ziqiang Cui, Yunpeng Weng, Xing Tang, Fuyuan Lyu, Dugang Liu, Xiuqiang He, Chen Ma
Venue: SIGIR
Year: 2025

In recent years, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items. However, existing methods still face several limitations. First, most KGs suffer from missing facts or limited scopes. Second, existing methods convert textual information in KGs into IDs, resulting in the loss of natural semantic connections between different items. Third, existing methods struggle to capture high-order connections in the global KG. To address these limitations, we propose a novel method called CoLaKG, which leverages large language models (LLMs) to improve KG-based recommendations. The extensive knowledge and remarkable reasoning capabilities of LLMs enable our method to supplement missing facts in KGs, and their powerful text understanding abilities allow for better utilization of semantic information. Specifically, CoLaKG extracts useful information from KGs at both local and global levels. By employing the item-centered subgraph extraction and prompt engineering, it can accurately understand the local information. In addition, through the semantic-based retrieval module, each item is enriched by related items from the entire knowledge graph, effectively harnessing global information. Furthermore, the local and global information are effectively integrated into the recommendation model through a representation fusion module and a retrieval-augmented representation learning module, respectively. Extensive experiments on four real-world datasets demonstrate the superiority of our method.
Mitigating Source Bias with LLM Alignment
Authors: Sunhao Dai, Yuqi Zhou, Liang Pang, Zhuoyang Li, Zhaocheng Du, Gang Wang, Jun Xu
Venue: SIGIR
Year: 2025

Recent studies have revealed a phenomenon known as source bias, where PLM-based retrievers assign higher relevance scores to LLM-generated content despite its semantic quality being comparable to human-written content. As LLMs rapidly advance and become more widely used, effectively counteracting source bias is crucial for the sustainable development of the information retrieval (IR) ecosystem. Existing methods primarily attempt to address source bias from the retriever side, adopting a "passive defense" approach that intervenes only after biased content has entered the retrieval pipeline. These solutions are limited by frequent retriever updates in industrial applications, high recurring costs, and their inability to address the root cause of source bias. In this paper, we propose a new perspective for mitigating source bias by actively aligning LLM outputs at the data generation stage. Specifically, we introduce LLM-SBM, a novel LLM alignment framework for source bias mitigation. First, we construct high-quality alignment datasets using an automatic preference data construction pipeline. This pipeline leverages LLMs to generate multiple rephrasings of content and employs a PLM-based retriever to assign corresponding specific preference values for each generated document, thereby forming preference pairs according to these preferences. Moreover, to fully utilize these scalar values of preference and enhance the efficiency of the alignment process, LLM-SBM incorporates these preference differences as weighting factors in the loss function during policy training. Extensive experiments across multiple datasets and PLM-based retrievers demonstrate that LLMs aligned with LLM-SBM successfully reduce source bias while preserving their general capabilities.
NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search
Authors: Sunhao Dai, Wenjie Wang, Liang Pang, Jun Xu, See-Kiong Ng, Ji-Rong Wen, Tat-Seng Chua
Venue: SIGIR
Year: 2025

Generative AI search driven by large language models (LLMs) is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple web pages. However, while this paradigm enhances convenience, it disrupts the feedback-driven improvement loop that has historically powered the evolution of traditional Web search. Web search can continuously improve their ranking models by collecting large-scale, fine-grained user feedback (e.g., clicks, dwell time) at the document level. In contrast, generative AI search operates through a much longer search pipeline-spanning query decomposition, document retrieval, and answer generation-yet typically receives only coarse-grained feedback on the final answer. This introduces a feedback loop disconnect, where user feedback for the final output cannot be effectively mapped back to specific system components, making it difficult to improve each intermediate stage and sustain the feedback loop. To address this limitation, we envision NExT-Search, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search. NExT-Search integrates two complementary modes: User Debug Mode, which allows engaged users to intervene at key stages-such as refining query decomposition, rating retrieved documents, and editing initial generated responses-and Shadow User Mode, where a personalized user agent simulates user preferences and provides AI-assisted feedback for less interactive users. As these feedback signals serve as valuable resources for refining the whole search pipeline, we also introduce a feedback store mechanism that encourages users to share and monetize their debugging efforts, further incentivizing participation. Furthermore, we envision how these feedback signals can be leveraged through online adaptation, which refines current search outputs in real-time, and offline update, which aggregates interaction logs to periodically fine-tune query decomposition, retrieval, and generation models. By restoring human control over key stages of the generative AI search pipeline, we believe NExT-Search offers a promising direction for building feedback-rich AI search systems that can evolve continuously alongside human feedback.