OpenRaiser/Intern-Atlas huggingface dataset:
Please meet AI, Our Dear New Colleague. In other Words: Can Scientists and Machines Truly Cooperate?
Authors: Iryna Gurevych
Venue: SIGIR
Year: 2025

How can AI and LLMs facilitate the work of scientists in different stages of the research process? Can technology even make scientists obsolete? The role of AI and Large Language Models (LLMs) in science as the target application domain has recently been rapidly growing. This includes assessing the impact of scientific work, facilitating writing and revising manuscripts as well as intelligent support for manuscript quality assessment, peer-review and scientific discussions. The talk will illustrate such methods and models using several tasks from the scientific domain. We argue that while AI and LLMs can effectively support and augment specific steps of the research process, expert-AI collaboration may be a more promising mode for complex research tasks.
X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation
Authors: Guy Hadad, Haggai Roitman, Yotam Eshel, Bracha Shapira, Lior Rokach
Venue: SIGIR
Year: 2025

As new products are emerging daily, recommendation systems are required to quickly adapt to possible new domains without needing extensive retraining. This work presents ''X-Cross'' -- a novel cross-domain sequential-recommendation model that recommends products in new domains by integrating several domain-specific language models; each model is fine-tuned with low-rank adapters (LoRA). Given a recommendation prompt, operating layer by layer, X-Cross dynamically refines the representation of each source language model by integrating knowledge from all other models. These refined representations are propagated from one layer to the next, leveraging the activations from each domain adapter to ensure domain-specific nuances are preserved while enabling adaptability across domains. Using Amazon datasets for sequential recommendation, X-Cross achieves performance comparable to a model that is fine-tuned with LoRA, while using only 25% of the additional parameters. In cross-domain tasks, such as adapting from Toys domain to Tools, Electronics or Sports, X-Cross demonstrates robust performance, while requiring about 50%-75% less fine-tuning data than LoRA to make fine-tuning effective. Furthermore, X-Cross achieves significant improvement in accuracy over alternative cross-domain baselines. Overall, X-Cross enables scalable and adaptive cross-domain recommendations, reducing computational overhead and providing an efficient solution for data-constrained environments.
TIREx Tracker: The Information Retrieval Experiment Tracker
Authors: Tim Hagen, Maik Fröbe, Jan Heinrich Merker, Harrisen Scells, Matthias Hagen, Martin Potthast
Venue: SIGIR
Year: 2025

The reproducibility and transparency of retrieval experiments depends on the availability of information about the experimental setup. However, the manual collection of experiment metadata can be tedious, error-prone, and inconsistent, which calls for an automated systematic collection. Expanding ir_metadata, we present the TIREx tracker, a tool that records hardware configurations, power/CPU/RAM/GPU usage, and experiment/system versions. Implemented as a lightweight platform-independent C binary, the TIREx tracker integrates seamlessly into Python, Java, or C/C++ workflows and can be easily integrated into shard task submissions, as we demonstrate for the TIRA/TIREx platform. Code, binaries, and documentation of the TIREx tracker are publicly available at https://github.com/tira-io/tirex-tracker.
Navigating Speech Recording Collections with AI-Generated Illustrations
Authors: Sirina Håland, Trond Karlsen Strøm, Petra Galuscáková
Venue: SIGIR
Year: 2025

Although the amount of available spoken content is steadily increasing, extracting information and knowledge from speech recordings remains challenging. Beyond enhancing traditional information retrieval methods such as speech search and keyword spotting, novel approaches for navigating and searching spoken content need to be explored and developed. In this paper, we propose a novel navigational method for speech archives that leverages recent advances in language and multimodal generative models. We demonstrate our approach with a Web application that organizes data into a structured format using interactive mind maps and image generation tools. The system is implemented using the TED-LIUM~3 dataset, which comprises over 2,000 speech transcripts and audio files of TED Talks. Initial user tests using a System Usability Scale (SUS) questionnaire indicate the application's potential to simplify the exploration of large speech collections.
FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation
Authors: Mingzhe Han, Dongsheng Li, Jiafeng Xia, Jiahao Liu, Hansu Gu, Peng Zhang, Ning Gu, Tun Lu
Venue: SIGIR
Year: 2025

Recommendation algorithms rely on user historical interactions to deliver personalized suggestions, which raises significant privacy concerns. Federated recommendation algorithms tackle this issue by combining local model training with server-side model aggregation, where most existing algorithms use a uniform weighted summation to aggregate item embeddings from different client models. This approach has three major limitations: 1) information loss during aggregation, 2) failure to retain personalized local features, and 3) incompatibility with parameter-free recommendation algorithms. To address these limitations, we first review the development of recommendation algorithms and recognize that their core function is to share collaborative information, specifically the global relationship between users and items. With this understanding, we propose a novel aggregation paradigm named collaborative information aggregation, which focuses on sharing collaborative information rather than item parameters. Based on this new paradigm, we introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation. This method requires each client to upload item similarity matrices for aggregation, which allows clients to align their local models without constraining embeddings to a unified vector space. As a result, it mitigates information loss caused by direct summation, preserves the personalized embedding distributions of individual clients, and supports the aggregation of parameter-free models. Theoretical analysis and experimental results on real-world datasets demonstrate the superior performance of FedCIA compared with the state-of-the-art federated recommendation algorithms. Code is available at https://github.com/Mingzhe-Han/FedCIA.
Content Moderation in TV Search: Balancing Policy Compliance, Relevance, and User Experience
Authors: Adeep Hande, Kishorekumar Sundararajan, Sardar Hamidian, Ferhan Ture
Venue: SIGIR
Year: 2025

Millions of people rely on search functionality to find and explore content on entertainment platforms. Modern search systems use a combination of candidate generation and ranking approaches, with advanced methods leveraging deep learning and LLM-based techniques to retrieve, generate, and categorize search results. Despite these advancements, search algorithms can still surface inappropriate or irrelevant content due to factors like model unpredictability, metadata errors, or overlooked design flaws. Such issues can misalign with product goals and user expectations, potentially harming user trust and business outcomes. In this work, we introduce an additional monitoring layer using Large Language Models (LLMs) to enhance content moderation. This additional layer flags content if the user did not intend to search for it. This approach serves as a baseline for product quality assurance, with collected feedback used to refine the initial retrieval mechanisms of the search model, ensuring a safer and more reliable user experience.
Search Efficiency for Score-at-a-Time Retrieval
Authors: Katelyn Harlan
Venue: SIGIR
Year: 2025

Impact-ordered indexes, and thus Score-at-a-Time (SaaT) retrieval, is seeing a resurgence due to the emergence of learned sparse representations [4] (LSR) which employ the use of neural networks to learn the term-document weights. Within this context, SaaT has been found to be a competitive alternative to the more studied Document-at-a-Time (DaaT) approach. Even so, JASSv2 [2, 6] and IOQP [4] remain the only well-known open-source SaaT search engines.We believe that a thorough study into their operation and components may provide valuable insights. We wish to use this knowledge to build a better baseline system for SaaT retrieval. Once established, we aim to improve upon it. Specifically, we ask: (1) Can we build a better baseline for Score-at-a-Time retrieval by finding the most efficient and effective components from existing systems? (2) Can we improve upon the baseline by updating QMX to make use of more recent SIMD technologies? (3) Can we improve upon the baseline with a new accumulator management strategy? (4) Can we improve upon the baseline with a new approach to early termination/pruning? It is not obvious what the current baseline for SaaT should be, or what components are the most efficient. Through investigating various aspects of the search engines - specifically integer compression, accumulator management, and early termination (ρ) - we will build a more indicative baseline system. We highlight these components in particular as we have reason to believe they will be influential.
Large Models are Good Annotators for Zero-Shot Learning
Authors: Qingzhi He, Yizhen Jia, Wentong Li, Shengcai Liao, Rong Quan, Tong Cui, Jie Qin
Venue: SIGIR
Year: 2025

Human-annotated attributes serve as effective semantic label embeddings for zero-shot learning (ZSL); however, their annotation is labor-intensive and difficult to scale. Recent studies have explored weakly supervised semantic label embeddings to reduce human effort, but these methods often fail to capture visual similarity and underperform compared to human-annotated semantics. In this work, we propose a minimally supervised yet effective approach: GPT- and CLIP-powered attributes (GCAtt). Specifically, we introduce a three-step interaction process with ChatGPT-comprising preliminary design, hierarchical refinement, and specific value determination-to generate attributes that are both category-shared and discriminative for classification. Additionally, we develop a method that encodes attributes and their values as potential text pairings, leveraging CLIP's retrieval capabilities for annotation. Experimental results on four widely used benchmarks demonstrate that GCAtt consistently outperforms human-annotated semantics. Code and data are available at https://github.com/RowenaHe/GCAtt.
Tip of the Tongue Query Elicitation for Simulated Evaluation
Authors: Yifan He, To Eun Kim, Fernando Diaz, Jaime Arguello, Bhaskar Mitra
Venue: SIGIR
Year: 2025

Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effectively support TOT scenarios. Research on TOT retrieval is further constrained by the challenge of collecting queries, as current approaches rely heavily on community question-answering (CQA) websites, leading to labor-intensive evaluation and domain bias. To overcome these limitations, we introduce two methods for eliciting TOT queries-leveraging large language models (LLMs) and human participants-to facilitate simulated evaluations of TOT retrieval systems. Our LLM-based TOT user simulator generates synthetic TOT queries at scale, achieving high correlations with how CQA-based TOT queries rank TOT retrieval systems when tested in the Movie domain. Additionally, these synthetic queries exhibit high linguistic similarity to CQA-derived queries. For human-elicited queries, we developed an interface that uses visual stimuli to place participants in a TOT state, enabling the collection of natural queries. In the Movie domain, system rank correlation and linguistic similarity analyses confirm that human-elicited queries are both effective and closely resemble CQA-based queries. These approaches reduce reliance on CQA-based data collection while expanding coverage to underrepresented domains, such as Landmark and Person. LLM-elicited queries for the Movie, Landmark, and Person domains have been released as test queries in the TREC 2024 TOT track, with human-elicited queries scheduled for inclusion in the TREC 2025 TOT track. Additionally, we provide source code for synthetic query generation and the human query collection interface, along with curated visual stimuli used for eliciting TOT queries.
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning
Authors: Tao He, Lizi Liao, Ming Liu, Bing Qin
Venue: SIGIR
Year: 2025

Recent advancements in dialogue policy planning have focused on optimizing system agent policies to achieve predefined goals, emphasizing strategy design, trajectory acquisition, and training efficiency. However, these approaches often overlook the critical role of user characteristics, which are essential in real-world scenarios like conversational search and recommendation, where interactions must adapt to individual user traits such as personality, preferences, and goals. To address this gap, we conduct a comprehensive study using task-specific user personas to evaluate dialogue policy planning under diverse user behaviors. Our analysis, based on these user profiles, reveals significant shortcomings in existing approaches, underscoring the necessity for user-tailored dialogue policies. Building on these insights, we propose the User-Tailored Dialogue Policy Planning (UDP) framework, which integrates an Intrinsic User World Model to capture user traits and feedback. UDP operates in three stages: (1) User Persona Portraying, employing a diffusion model to dynamically infer user profiles; (2) User Feedback Anticipating, using a Brownian Bridge-inspired mechanism to predict user reactions; and (3) User-Tailored Policy Planning, synthesizing these elements to optimize response strategies. To enhance robustness, we introduce an active learning approach that prioritizes challenging user personas during training. Extensive experiments across benchmarks, including both collaborative and non-collaborative settings, demonstrate UDP's effectiveness in learning user-specific dialogue strategies. Results confirm the framework's utility, highlighting its robustness, adaptability, and potential to advance user-centric dialogue systems.
Short Video Segment-level User Dynamic Interests Modeling in Personalized Recommendation
Authors: Zhiyu He, Zhixin Ling, Jiayu Li, Zhiqiang Guo, Weizhi Ma, Xinchen Luo, Min Zhang, Guorui Zhou
Venue: SIGIR
Year: 2025

The rapid growth of short videos has necessitated effective recommender systems to match users with content tailored to their evolving preferences. Current video recommendation models primarily treat each video as a whole, overlooking the dynamic nature of user preferences with specific video segments. In contrast, our research focuses on segment-level user interest modeling, which is crucial for understanding how users' preferences evolve during video browsing. To capture users' dynamic segment interests, we propose an innovative model that integrates a hybrid representation module, a multi-modal user-video encoder, and a segment interest decoder. Our model addresses the challenges of capturing dynamic interest patterns, missing segment-level labels, and fusing different modalities, achieving precise segment-level interest prediction. We present two downstream tasks to evaluate the effectiveness of our segment interest modeling approach: video-skip prediction and short video recommendation. Our experiments on real-world short video datasets with diverse modalities show promising results on both tasks. It demonstrates that segment-level interest modeling brings a deep understanding of user engagement and enhances video recommendations. We also release a unique dataset that includes segment-level video data and diverse user behaviors, enabling further research in segment-level interest modeling. This work pioneers a novel perspective on understanding user segment-level preference, offering the potential for more personalized and engaging short video experiences.
Characterising Topic Familiarity and Query Specificity Using Eye-Tracking Data
Authors: Jiaman He, Zikang Leng, Dana McKay, Johanne R. Trippas, Damiano Spina
Venue: SIGIR
Year: 2025

Eye-tracking data has been shown to correlate with a user's knowledge level and query formulation behaviour. While previous work has focused primarily on eye gaze fixations for attention analysis, often requiring additional contextual information, our study investigates the memory-related cognitive dimension by relying solely on pupil dilation and gaze velocity to infer users' topic familiarity and query specificity without needing any contextual information. Using eye-tracking data collected via a lab user study N=18, we achieved a Macro F1 score of 71.25% for predicting topic familiarity with a Gradient Boosting classifier, and a Macro F1 score of 60.54% with a k-nearest neighbours (KNN) classifier for query specificity. Furthermore, we developed a novel annotation guideline - specifically tailored for question answering - to manually classify queries as Specific or Non-specific. This study demonstrates the feasibility of eye-tracking to better understand topic familiarity and query specificity in search.
ULP: Unlabeled Location Prediction from Text
Authors: Xi He, Yilin Liu, Yijie Sun, Xin Xing, Xingyu Lu, Yanbing Liu
Venue: SIGIR
Year: 2025

With the popularity of smart mobile devices, location-based services (LBS) have been widely applied. Predicting geographical locations from text holds significant value for smart cities and personalized travel. Existing research primarily focuses on the retrieval or prediction of labeled locations, such as cities or points of interest (POIs). However, in scenarios like autonomous driving navigation and autonomous logistics delivery, it is necessary to precisely predict the coordinates of unlabeled locations, for example, 200 meters northwest of a certain location. Consequently, we introduce a new task to infer fine-grained unlabeled locations from text. This task is particularly challenging because of the ambiguous text and the semantic gap between geographic and textual modalities. In this paper, we aim to construct an end-to-end fine-grained location prediction model to accurately predict the unlabeled locations mentioned in texts. First, we encode the geographic coordinates and transform the location prediction problem into a geographic encoding generation problem. Second, we propose a multi-scale cross-modal loss (MCL) to learn the implicit mapping between geographic and textual modalities. Lastly, we design a multi-task prediction model ULP to predict the coordinates of unlabeled locations. We conducted experiments on two real-world datasets, and the results show that our proposed method outperforms existing state-of-the-art retrieval-based methods.
Low-Cost Document Retrieval with Dense Pseudo-Query Encoding
Authors: Shanxiu He, Wentai Xie, Yifan Qiao, Parker Carlson, Tao Yang
Venue: SIGIR
Year: 2025

Low-cost retrieval is crucial for document search on resource-limited computing platforms. This paper presents a staged sparse-to-dense retrieval framework that substitutes expensive dense query encoding with a dense pseudo-query (DPQ), an approximation derived solely from sparse retrieval results. DPQ scheme employs a simple, rank-aware weighting to combine corresponding dense representations of top sparse results, providing an opportunity to efficiently leverage an expensive but expressive LLM or BERT-based dense model without requiring GPUs. The evaluation demonstrates that DPQ-based retrieval runs fast on an affordable platform and outperforms several low-cost baselines in zero-shot retrieval.
Axiomatic Re-Ranking for Argument Retrieval
Authors: Maximilian Heinrich, Marvin Vogel, Alexander Bondarenko, Matthias Hagen, Benno Stein
Venue: SIGIR
Year: 2025

Information retrieval axioms are formalized constraints that retrieval systems should ideally satisfy (e.g., to rank documents higher that contain the query terms more often). In this paper, we propose new axioms that focus on the scenario of argument retrieval: retrieval for queries that need arguments in the results. Our underlying axiomatic idea is that in such scenarios, documents should be prioritized with argumentative units that are similar to the query. We test our new axioms in re-ranking experiments on the data of the Touché ~2020 and~2021 shared task on argument retrieval for controversial questions, and show that the new axioms can improve the effectiveness of Touché's strong DirichletLM baseline model and even of the top-performing system from Touché ~2021, a system already specifically optimized for argument retrieval. Finally, we also propose a new method for visualizing the relationships between axioms based on their effects in re-ranking settings.
Benchmark Granularity and Model Robustness for Image-Text Retrieval: A Reproducibility Study
Authors: Mariya Hendriksen, Shuo Zhang, Ridho Reinanda, Mohamed Yahya, Edgar Meij, Maarten de Rijke
Venue: SIGIR
Year: 2025

Image-Text Retrieval (ITR) systems are central to multimodal information access, with Vision-Language Models (VLMs) showing strong performance on standard benchmarks. However, these benchmarks predominantly rely on coarse-grained annotations, limiting their ability to reveal how models would perform under real-world conditions, where query granularity varies. Motivated by this gap, we examine how dataset granularity and query perturbations affect retrieval performance and robustness across four architecturally diverse VLMs (ALIGN, AltCLIP, CLIP, and GroupViT). Using both standard benchmarks (MS-COCO, Flickr30k) and their fine-grained variants, we show that richer captions consistently enhance retrieval, especially in text-to-image tasks, where we observe an average improvement of 16.23%, compared to 6.44% in image-to-text. To assess robustness, we introduce a taxonomy of perturbations and conduct extensive experiments, revealing that while perturbations typically degrade performance, they can also unexpectedly improve retrieval, exposing nuanced model behaviors. Notably, word order emerges as a critical factor - contradicting prior assumptions of model insensitivity to it. Our results highlight variation in model robustness and a dataset-dependent relationship between caption granularity and perturbation sensitivity and emphasize the necessity of evaluating models on datasets of varying granularity.
Workshop on Explainability in Information Retrieval
Authors: Maria Heuss, Catherine Chen, Avishek Anand, Carsten Eickhoff, Suzan Verberne
Venue: SIGIR
Year: 2025

As models grow more complex and societal demands for transparency increase with emerging regulations, explainability has become an even more important research area. However, despite its recognized relevance, explainability research in IR has seen slower progress than in related fields. This full day workshop aims to advance research in explainable information retrieval by providing a more in-depth platform to reflect on recent developments and facilitate discussions to address new and persistent challenges. Our goal is to bring together a diverse group of researchers to build a shared understanding of key tasks and challenges that will lay the foundation for the future of explainable IR research.