Accepted Papers

Query logs are key resources for studying search engine interactions and improving retrieval effectiveness but are rarely publicly available. In the past, search providers only shared small subsets of their own logs to curb competition and to ensure privacy. The Archive Query Log (AQL) will become a...

City councils play a crucial role in local governance, directly influencing citizens¡¯ daily lives through decisions made during municipal meetings. These deliberations are formally documented in meeting minutes, which serve as official records of discussions, decisions, and voting outcomes. Despi...

Different learned sparse retrieval (LSR) models offer different trade-offs between effectiveness and efficiency. However, while there are standardized and interoperable tools to assess LSR effectiveness, there is no agreed-upon methodology for evaluating efficiency, and datasets with high-quality re...

Previous research on group recommender systems (GRSs) has shown that group dynamics strongly influence decision-making, yet collaborative filtering (CF)¨Cbased GRSs rarely account for social interactions, largely because suitable analytical tools are lacking. This paper introduces a community resou...

Retrieval models are key components of Retrieval-Augmented Generation (RAG) systems, which generate search queries, process the documents returned, and generate a response. RAG systems are often dynamic and may involve multiple rounds of retrieval. While many state-of-the-art retrieval methods are a...

Resources for simulation-based evaluation of conversational recommender systems (CRSs) are scarce. The UserSimCRS toolkit was introduced to address this gap. In this work, we present UserSimCRS v2, a significant upgrade aligning the toolkit with state-of-the-art research. Key extensions include an e...

User simulators are essential for evaluating search systems, but they primarily copy user actions without understanding the underlying thought process. This gap exists because large-scale interaction logs record what users do, but not what they might be thinking or feeling, such as confusion or sati...

Fact-checking remains a demanding and time-consuming task, still largely dependent on manual verification and unable to match the rapid spread of misinformation online. This is particularly important because debunking false information typically takes longer to reach consumers than the original misi...

Expert-finding systems aim to identify knowledgeable individuals in specific domains based on evidence such as publications, activities, and social network data, with academic uses including allowing identification of potential supervisors, collaborators, or peer reviewers. However, most existing be...

We present a reusable dataset and accompanying infrastructure for studying human search behavior in Interactive Information Retrieval (IIR). The dataset combines detailed interaction logs from 61 participants (122 sessions) with user characteristics, including perceptual speed, topic-specific intere...

The use of natural language (NL) user profiles in recommender systems offers greater transparency and user control compared to traditional representations. However, there is scarcity of large-scale, publicly available test collections for evaluating NL profile-based recommendation. To address this g...

The rapid advancement of Large Language Models (LLMs) has transformed conversational systems into practical tools used by millions. However, the nature and necessity of information retrieval in real-world conversations remain largely unexplored, as research has focused predominantly on traditional, ...

This paper presents a principled and scalable framework for systematically generating complex Question Answering (QA) data. In the core of this framework is a graphlet-anchored generation process, where small subgraphs from a Knowledge Graph (KG) are used in a structured prompt to control the comple...

Dense retrieval systems have proven to be effective across various benchmarks, but require substantial memory to store large search indices. Recent advances in embedding compression show that index sizes can be greatly reduced with minimal loss in ranking quality. However, existing studies often ove...

Personalized food recommendation can promote healthier, sustainable eating, but current systems often rely on sparse and unstructured data, limiting semantic expressiveness and diverse personalization. In this paper, we propose FoodNexus, a large-scale knowledge graph with nearly one billion triples...

With the surge of multimodal models and the demand for effective image Information Retrieval (IR) systems, high-quality text-to-image datasets have become paramount. However, most existing datasets are primarily in English, limiting their applicability to multilingual settings. To address this, we i...

Validating user simulation is a difficult task due to the lack of established measures and benchmarks, which makes it challenging to assess whether a simulator accurately reflects real user behavior. As part of the Sim4IA Micro-Shared Task at the Sim4IA Workshop, SIGIR 2025, we present Sim4IA-Bench,...