Accepted Papers

A wealth of unstructured textual content implicitly holds geographic insights that are valuable across various domains, such as geographic search, disaster management, spatial humanities, and disease surveillance. These applications rely on different text types and languages, target different geogra...

Event extraction (EE) aims to identify event triggers and their corresponding arguments from unstructured text, providing structured knowledge essential for many downstream tasks. Despite the success of instruction-tuned large language models (LLMs), current methods often produce inconsistent format...

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...

Information Retrieval systems are becoming increasingly multimodal, and one interesting and natural modality for human information interaction is audio. Audio data is rapidly expanding in both volume and popularity. For example, as of September 2025, there are 4.52 million podcasts available worldwi...

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...

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...

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...

HIPE-2026 is a CLEF evaluation lab that focuses on person--place relation extraction from noisy, multilingual historical texts. Building on the previous HIPE campaigns in 2020 and 2022, this lab introduces a new task that targets semantic relations between persons and places. Specifically, systems a...

Similarity-based collaborative filtering (CF) models have long demonstrated strong offline performance and conceptual simplicity. However, their scalability is limited by the quadratic cost of maintaining dense item¨Citem similarity matrices. Partitioning-based paradigms have recently emerged as an...

Recommender systems shape many of our daily choices, including what we watch, purchase, and read. However, most existing systems interpret user behavior solely through observable actions such as clicks, views, or watch time, without considering how users actually feel when engaging with content. Thi...

The concept of relevance in Information Retrieval (IR) has been extensively studied. However, most mainstream IR models have been developed with adult users in mind, assuming cognitive maturity and autonomous interaction. Younger searchers, who increasingly integrate IR systems into their informatio...

Many analyses have been performed on Information Retrieval (IR) evaluation benchmarks, with many different approaches. Benchmarking also plays a central role in evaluating the capabilities of Large Language Models (LLMs). However, recent concerns have emerged regarding the robustness of benchmarks a...

The standard practice of query performance prediction (QPP) evaluation is to measure a set-level correlation between the estimated retrieval qualities and the true ones. However, this correlation-based evaluation measure quantifies QPP effectiveness at the level of individual queries, nor does this ...

Public libraries can potentially benefit by automated recommendation to increase patron engagement. Unlike commercial platforms, these publicly funded services have a responsibility to remain alert to biases that emerge in algorithmic recommendations. In this work, we conduct a systematic evaluation...

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...

We demonstrate CEDAR, an application for automating data science (DS) tasks with an agentic setup. Solving DS problems with LLMs is an underexplored area that has immense market value. The challenges are manifold: task complexities, data sizes, computational limitations, and context restrictions. We...

In sequential recommendation (SR), the self-attention mechanism of Transformer-based models acts as a low-pass filter, limiting their ability to capture high-frequency signals that reflect short-term user interests. To overcome this, BSARec augments the Transformer encoder with a frequency layer tha...

This paper introduces AgriIR, a configurable retrieval augmented generation (RAG) framework designed to deliver grounded, domain-specific answers while maintaining flexibility and low computational cost. Instead of relying on large, monolithic models, AgriIR decomposes the information access process...

Learned Sparse Retrieval (LSR) methods construct sparse lexical representations of queries and documents that can be efficiently searched using inverted indexes. Existing LSR approaches have almost exclusively relied on uncased backbone models, whose vocabularies exclude case-sensitive distinctions,...

ultimodal recommendation has emerged as a mainstream paradigm, typically leveraging text and visual embeddings extracted from pre-trained models such as Sentence-BERT, Vision Transformers, and ResNet. This approach is founded on the intuitive assumption that incorporating multimodal embeddings can e...

Over the past thirteen years, the large-scale biomedical semantic indexing and question-answering challenge (BioASQ) has consistently driven the advancement of methods and tools that enhance access to the rapidly growing body of scientific resources in the biomedical domain. BioASQ offers a unique c...

Modern ranking systems must comply with emerging AI regulations while resisting manipulation attacks. The EU AI Act's prohibition on individual user scoring creates a critical gap: reputation-based systems often violate compliance requirements, while user-agnostic approaches lack resistance to bribe...

The search for knowledgeable people is essential in academic settings, supporting the identification of suitable supervisors, reviewers, and collaborators. However, existing systems primarily focus on topical relevance while overlooking contextual factors such as supervision capacity, collaboration ...