OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Grained Patch Training for Efficient LLM-based Recommendation
Authors: Jiayi Liao, Ruobing Xie, Sihang Li, Xiang Wang, Xingwu Sun, Zhanhui Kang, Xiangnan He
Venue: SIGIR
Year: 2025

Large Language Models (LLMs) have emerged as a new paradigm for recommendation by converting interacted item history into language modeling. However, constrained by the limited context length of LLMs, existing approaches have to truncate item history in the prompt, focusing only on recent interactions and sacrificing the ability to model long-term history. To enable LLMs to model long histories, we pursue a concise embedding representation for items and sessions. In the LLM embedding space, we construct an item's embedding by aggregating its textual token embeddings; similarly, we construct a session's embedding by aggregating its item embeddings. While efficient, this way poses two challenges since it ignores the temporal significance of user interactions and LLMs do not natively interpret our custom embeddings. To overcome these, we propose PatchRec, a multi-grained patch training method consisting of two stages: (1) Patch Pre-training, which familiarizes LLMs with aggregated embeddings -- patches, and (2) Patch Fine-tuning, which enables LLMs to capture time-aware significance in interaction history. Extensive experiments show that PatchRec effectively models longer behavior histories with improved efficiency. This work facilitates the practical use of LLMs for modeling long behavior histories.
Mitigating Distribution Shifts in Sequential Recommendation: An Invariance Perspective
Authors: Yuxin Liao, Yonghui Yang, Min Hou, Le Wu, Hefei Xu, Hao Liu
Venue: SIGIR
Year: 2025

Sequential recommendation aims to learn users' dynamic preferences from their historical interactions and predict the next item they are most likely to engage with. In real-world scenarios, time-varying factors (e.g., product promotions, seasonal changes) induce distribution shifts in user interactions. Despite the demonstrated success of existing models, their generalization capability remains limited under such dynamic conditions. Current methods tackle this challenge by leveraging distributionally robust optimization (DRO) to optimize the "worst-case" loss or by employing manually designed data augmentation to enrich the training distribution. Despite their effectiveness, DRO-based approaches are inherently constrained by the sparsity of training data, limiting the range of distributions they can model, while manually designed augmentations risk introducing noise or irrelevant information that could distort user preference learning. Furthermore, these methods often overlook the sensitivity of user interactions to distribution shifts, which is essential for capturing the stable factors in the evolution of user preferences in real-world settings.
Gosling Grows Up: Retrieval with Learned Dense and Sparse Representations Using Anserini
Authors: Jimmy Lin, Arthur Haonan Chen, Carlos Lassance, Xueguang Ma, Ronak Pradeep, Tommaso Teofili, Jasper Xian, Jheng-Hong Yang, Brayden Zhong, Vincent Zhong
Venue: SIGIR
Year: 2025

The Anserini IR toolkit has come a long way since efforts began in 2015. Although the goals of the project - to bridge research and practice in information retrieval, and to provide reproducible, easy-to-use baselines - have remained constant, the world has changed quite a bit. We discuss how Anserini has evolved in response to this changing environment, the most significant of which is the advent of transformer-based retrieval models that did not exist when the project started. The bi-encoder architecture provides a framework for understanding retrieval models based on dense and sparse vector representations, and offers a reference for conveying the capabilities of our toolkit. Anserini provides end-to-end first-stage retrieval based on single-vector learned dense and sparse representations, directly building on the open-source Lucene search library and the ONNX runtime. This minimal design accelerates the pace of research and fosters reproducibility, enabling ''two-click reproductions''. By better aligning research and practice, we increase the potential real-world impact of research innovations.
Towards Interest Drift-driven User Representation Learning in Sequential Recommendation
Authors: Xiaolin Lin, Weike Pan, Zhong Ming
Venue: SIGIR
Year: 2025

Sequential recommendation (SR) aims to infer users' future interests and suggest the next items for them. Most SR methods learn one single vector to represent a user's recent interests, i.e., a user representation. Despite their great success, most of them do not explicitly consider the phenomenon of users' interest drift when learning user representations. Moreover, interest drift presents two critical challenges for these SR methods: (1) how to explore the potential distributions of the users' varying interest drift levels; and (2) how to capture the interest drift-aware collaborative knowledge among the users. In this paper, we delve into the issue of interest drift in SR and propose a novel and generic framework, i.e., Interest Drift-driven User Representation Learning (IDURL), to enhance SR methods to tackle the above two challenges. Specifically, our IDURL contains an interest drift quantization (IDQ) module to enable a quantitative measurement of the interest drift. Moreover, a drift representation generation module models the users' latent varying levels of interest drift, and an interest drift-guided representation disentanglement module optimizes the distributions of the interest drift levels under the guidance of IDQ. Furthermore, an interest drift-aware representation alignment module helps to capture the interest drift-aware collaborative knowledge among users. Finally, the users' overall interest representations are obtained to calculate the preference scores on the candidate items. Extensive experiments on four public datasets show the effectiveness of our IDURL. The source code of our IDURL is available at: https://github.com/xiaolLIN/IDURL.
BotBR: Social Bot Detection with Balanced Feature Fusion and Reliability-Enhanced Graph Learning
Authors: Qilong Lin, Jingya Zhou
Venue: SIGIR
Year: 2025

The rise of social bots poses a significant threat to online platforms, making their detection an urgent priority. Recent advancements in Graph Neural Networks (GNNs) have significantly improved bot detection by leveraging the rich relational data within social networks. However, existing approaches face two key challenges: imbalanced feature fusion across different modalities and edge heterophily, which limit their effectiveness. To address these issues, we propose BotBR, a novel bot detection framework. BotBR tackles feature imbalance by employing decision trees to extract behavioral patterns from user-related numerical features and utilizes an attention mechanism to seamlessly integrate multi-modal feature embeddings. To mitigate edge heterophily, BotBR incorporates an edge detector to differentiate between high- and low-reliability edges, optimizing the utilization of structural graph information. Furthermore, a homophily-based graph is introduced for consistency contrastive learning, enhancing the model's robustness. Experimental results on real-world bot detection benchmark datasets demonstrate that BotBR achieves state-of-the-art performance while maintaining efficiency comparable to classical methods.
Improving Link Sign Prediction in Signed Bipartite Graphs via Balanced Line Graphs
Authors: Hongxiang Lin, Yixiao Zhou, Huiying Hu, Xiaoqing Lyu
Venue: SIGIR
Year: 2025

Signed bipartite graphs are widely employed in social networks, recommender systems, and other domains, where the nodes represent two different sets, such as users and commodities, and the links have positive and negative signs to reflect the ratings. Link sign prediction is a crucial task, but the link class imbalance when one type of link (e.g., the head class) is significantly more numerous than another (e.g., the tail class) makes this task extremely challenging. To address this challenge, we propose a Line-Graph-Based Dynamic Balancing Prediction (LDBP) method. Specifically, we first convert the links of a signed bipartite graph into nodes of a line graph, and then the link class imbalance problem in the bipartite graph is naturally transformed to the problem of node class imbalance in the line graph. To tackle this problem, we introduce a Centroid Contrastive Learning (CCL) method and design a Dynamic Synthesis & Deletion (DSD) strategy for the tail-class nodes. By dynamically adjusting the quantity of synthesized tail-class nodes, we obtain a Balanced Line Graph (BaLG). Extensive experiments on real-world datasets demonstrate the effectiveness of our method in improving the accuracy of link sign prediction and addressing the issue of link class imbalance.
Multi-Interest Matching for Personalized News Recommendation with Large Language Models
Authors: Hongxiang Lin, Yixiao Zhou, Huiying Hu, Xiaoqing Lyu
Venue: SIGIR
Year: 2025

Personalized news recommendation plays a vital role in mitigating information overload, yet challenges persist in accurately capturing user preferences and fine-grained interests. Leveraging the semantic understanding and extraction capabilities of large language models (LLMs), we propose a Multi-Interest Personalized News Recommendation (MIPNR) model to address these issues. MIPNR separately models user interests at the user, news, and entity levels. Specifically, we introduce a Category-Guided Interest-News Matching (CGIN-Matching) method to identify potential interests, a Local News Entity Graph (LNEG) to model subtle entity relationships, and an entity-wise attention mechanism to extract fine-grained interests. In addition, LLMs are used to generate explicit textual descriptions of user preferences. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach.
Unbiased Collaborative Filtering with Fair Sampling
Authors: Jiahao Liu, Dongsheng Li, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu
Venue: SIGIR
Year: 2025

Recommender systems leverage extensive user interaction data to model preferences; however, directly modeling these data may introduce biases that disproportionately favor popular items. In this paper, we demonstrate that popularity bias arises from the influence of propensity factors during training. Building on this insight, we propose a fair sampling (FS) method that ensures each user and each item has an equal likelihood of being selected as both positive and negative instances, thereby mitigating the influence of propensity factors. The proposed FS method does not require estimating propensity scores, thus avoiding the risk of failing to fully eliminate popularity bias caused by estimation inaccuracies. Comprehensive experiments demonstrate that the proposed FS method achieves state-of-the-art performance in both point-wise and pair-wise recommendation tasks. The code implementation is available at https://github.com/jhliu0807/Fair-Sampling.
On the Scaling of Robustness and Effectiveness in Dense Retrieval
Authors: Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng
Venue: SIGIR
Year: 2025

Robustness and Effectiveness are critical aspects of developing dense retrieval models for real-world applications. It is known that there is a trade-off between the two. Recent work has addressed scaling laws of effectiveness in dense retrieval, revealing a power-law relationship between effectiveness and the size of models and data. Does robustness follow scaling laws too? If so, can scaling improve both robustness and effectiveness together, or do they remain locked in a trade-off? To answer these questions, we conduct a comprehensive experimental study. We find that: (i) Robustness, including out-of-distribution and adversarial robustness, also follows a scaling law. (ii) Robustness and effectiveness exhibit different scaling patterns, leading to significant resource costs when jointly improving both. Given these findings, we shift to the third factor that affects model performance, namely the optimization strategy, beyond the model size and data size. We find that: (i) By fitting different optimization strategies, the joint performance of robustness and effectiveness traces out a Pareto frontier. (ii) When the optimization strategy strays from Pareto efficiency, the joint performance scales in a sub-optimal direction. (iii) By adjusting the optimization weights to fit the Pareto efficiency, we can achieve Pareto training, where the scaling of joint performance becomes most efficient. Even without requiring additional resources, Pareto training is comparable to the performance of scaling resources several times under optimization strategies that overly prioritize either robustness or effectiveness. Finally, we demonstrate that our findings can help deploy dense retrieval models in real-world applications that scale efficiently and are balanced for robustness and effectiveness.
AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations
Authors: Jiahao Liu, Shengkang Gu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu
Venue: SIGIR
Year: 2025

LLM-based user agents, which simulate user interaction behavior, are emerging as a promising approach to enhancing recommender systems. In real-world scenarios, users' interactions often exhibit cross-domain characteristics and are influenced by others. However, the memory design in current methods causes user agents to introduce significant irrelevant information during decision-making in cross-domain scenarios and makes them unable to recognize the influence of other users' interactions, such as popularity factors. To tackle this issue, we propose a dual-layer memory architecture combined with a two-step fusion mechanism. This design avoids irrelevant information during decision-making while ensuring effective integration of cross-domain preferences. We also introduce the concepts of interest groups and group-shared memory to better capture the influence of popularity factors on users with similar interests. Comprehensive experiments validate the effectiveness of AgentCF++. Our code is available at https://github.com/jhliu0807/AgentCF-plus.
Advancing Ship Re-Identification in the Wild: The ShipReID-2400 Benchmark Dataset and D2InterNet Baseline Method
Authors: Baolong Liu, Roukai Huang, Xin Pan, Chuanhuang Li, Jie Sun, Jianfeng Dong, Xun Wang
Venue: SIGIR
Year: 2025

Ship Re-Identification (ReID) aims to accurately identify ships with the same identity across different times and camera views, playing a crucial role in intelligent waterway transportation. However, compared to the widely researched pedestrian and vehicle ReID, Ship ReID has received much less attention, primarily due to the scarcity of large-scale and high-quality ship ReID datasets available for public access. Moreover, several unique challenges make ship ReID particularly difficult: ships are large objects that are hard to capture fully, and the visible area of ships vary significantly due to changes in cargo loading or water surface conditions. These challenges make it difficult to achieve ideal results by directly applying existing ReID methods. To address these challenges, in this paper, we introduce ShipReID-2400, a dataset for ship ReID compiled from a real-world intelligent waterway traffic monitoring system. It comprises 17,241 images of 2,400 distinct ship identities collected over 53 months, ensuring diversity and representativeness. Furthermore, we propose the Disentangle-to-Interact Network ( D2InterNet ), a simple but strong baseline for ship ReID designed to extract discriminative local features despite significant scale variations. Extensive experimental results show that D2InterNet achieves state-of-the-art performance on both the ShipReID-2400 and VesselReID datasets. In addition, despite being designed for ship ReID, D2InterNet also achieves competitive results on the MSMT17 pedestrian ReID dataset, showcasing its good generalization capability. Our dataset and code are publicly available at https://github.com/HuiGuanLab/ShipReID-2400.
Gaming for Boundary: Elastic Localization for Frame-Supervised Video Moment Retrieval
Authors: Hao Liu, Yupeng Hu, Kun Wang, Yinwei Wei, Liqiang Nie
Venue: SIGIR
Year: 2025

Video moment retrieval aims to determine the temporal boundaries of moments within a video that are most relevant to textual queries. Unlike fully-supervised and weakly-supervised methods, frame-supervised methods use a single annotated frame to model the similarities between the target moment and queries. This task is still in its infancy due to the following challenges: 1) indiscernible intra-modal information and 2) inflexible inter-modal information interaction. In light of these challenges, we introduce the Gaming fOr elAstic Localization (GOAL) method for frame-supervised video moment retrieval. It enables target moment boundary localization from a novel strategic game perspective. GOAL encompasses two core components: a game-based paradigm to find the most reliable moment and a Dynamic Updating Technique (DUT) to continuously optimize moment retrieval through dynamic gradients, thereby refining boundary predictions with different feedback. Extensive experiments on Charades-STA, ActivityNet Captions, and TACoS have validated the effectiveness of GOAL.
The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News
Authors: Yuhan Liu, Yuxuan Liu, Xiaoqing Zhang, Xiuying Chen, Rui Yan
Venue: SIGIR
Year: 2025

In today's digital environment, the rapid propagation of fake news via social networks poses significant social challenges. Most existing detection methods either employ traditional classification models, which suffer from low interpretability and limited generalization capabilities, or craft specific prompts for large language models (LLMs) to produce explanations and results directly, failing to leverage LLMs' reasoning abilities fully. Inspired by the saying that ''truth becomes clearer through debate,'' our study introduces a novel multi-agent system with LLMs named TruEDebate (TED) to enhance the interpretability and effectiveness of fake news detection. TED employs a rigorous debate process inspired by formal debate settings. Central to our approach are two innovative components: the DebateFlow Agents and the InsightFlow Agents. The DebateFlow Agents organize agents into two teams, where one supports and the other challenges the truth of the news. These agents engage in opening statements, cross-examination, rebuttal, and closing statements, simulating a rigorous debate process akin to human discourse analysis, allowing for a thorough evaluation of news content. Concurrently, the InsightFlow Agents consist of two specialized sub-agents: the Synthesis Agent and the Analysis Agent. The Synthesis Agent summarizes the debates and provides an overarching viewpoint, ensuring a coherent and comprehensive evaluation. The Analysis Agent, which includes a role-aware encoder and a debate graph, integrates role embeddings and models the interactions between debate roles and arguments using an attention mechanism, providing the final judgment.Our extensive experiments on two datasets, ARG-EN and ARG-CN, demonstrate that the TED framework surpasses traditional methods across various metrics and, more importantly, enhances interpretable fake news detection by illuminating logical reasoning and structured debate processes leading to accurate conclusions.We release our code to support Information systems that use structured debate within responsible information systems for improved decision-making.
Robust-IR @ SIGIR 2025: The First Workshop on Robust Information Retrieval
Authors: Yu-An Liu, Haya Nachimovsky, Ruqing Zhang, Oren Kurland, Jiafeng Guo, Moshe Tennenholtz
Venue: SIGIR
Year: 2025

With the advancement of information retrieval (IR) technologies, robustness is increasingly attracting attention. When deploying technology into practice, we consider not only its average performance under normal conditions but, more importantly, its ability to maintain functionality across a variety of exceptional situations. In recent years, the research on IR robustness covers theory, evaluation, methodology, and application, and all of them show a growing trend. The purpose of this workshop is to systematize the latest results of each research aspect, to foster comprehensive communication within this niche domain while also bridging robust IR research with the broader community, and to promote further future development of robust IR. To avoid the one-sided talk of mini-conferences, this workshop adopts a highly interactive format, including round-table and panel discussion sessions, to encourage active participation and meaningful exchange among attendees.
NAM: A Normalization Attention Model for Personalized Product Search In Fliggy
Authors: Shui Liu, Mingyuan Tao, Maofei Que, Pan Li, Dong Li, Shenghua Ni, Zhuoran Zhuang
Venue: SIGIR
Year: 2025

Personalized product search provides significant benefits to e-commerce platforms by extracting more accurate user preferences from historical behaviors. Previous studies largely focused on the user factors when personalizing the search query, while ignoring the item perspective, which leads to the following two challenges that we summarize in this paper: First, previous approaches relying only on co-occurrence frequency tend to overestimate the conversion rates for popular items and underestimate those for long-tail items, resulting in inaccurate item similarities; Second, user purchasing propensity is highly heterogeneous according to the popularity of the target item: it is less correlated with the user's historical behavior for a popular item and more correlated for a long-tail item. To address these challenges, in this paper we propose NAM, a Normalization Attention Model, which optimizes ''when to personalize'' by utilizing Inverse Item Frequency (IIF) and employing a gating mechanism, as well as optimizes ''how to personalize'' by normalizing the attention mechanism from a global perspective. Through comprehensive experiments, we demonstrate that our proposed NAM model significantly outperforms state-of-the-art baseline models. Furthermore, we conducted an online A/B test at Fliggy, and obtained a significant improvement of 0.8% over the latest production system in conversion rate.
Queries Are Not Alone: Clustering Text Embeddings for Video Search
Authors: Peiyang Liu, Xi Wang, Ziqiang Cui, Wei Ye
Venue: SIGIR
Year: 2025

The rapid proliferation of video content across various platforms has highlighted the urgent need for advanced video retrieval systems. Traditional methods, which primarily depend on directly matching textual queries with video metadata, often fail to bridge the semantic gap between text descriptions and the multifaceted nature of video content. This paper introduces a novel framework, the Video-Text Cluster (VTC), which enhances video retrieval by clustering text queries to capture a broader semantic scope. We propose a unique clustering mechanism that groups related queries, enabling our system to consider multiple interpretations and nuances of each query. This clustering is further refined by our innovative Sweeper module, which identifies and mitigates noise within these clusters. Additionally, we introduce the Video-Text Cluster-Attention (VTC-Att) mechanism, which dynamically adjusts focus within the clusters based on the video content, ensuring that the retrieval process emphasizes the most relevant textual features. Further experiments have demonstrated that our proposed model surpasses existing state-of-the-art models on five public datasets.