Accepted Papers

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...

This paper tackles the problem of time-to-eventcounterfactual survival prediction, aiming to optimizeindividualized survival outcomes in the presence ofheterogeneity and censored data. We propose CURE, aframework that advances counterfactual survival modelingvia comprehensive multimodal embedding an...

While reasoning rerankers, such as Rank1, have demonstrated strong abilities in improving ranking relevance, it is unclear how they perform on other retrieval qualities such as fairness. We conduct the first systematic comparison of fairness between reasoning and non-reasoning rerankers. Using the T...

Recommender systems (RS) increasingly mediate access to crucial life spheres like employment and education, yet most remain optimized for short-term engagement rather than long-term user empowerment. While recent trustworthy RS research has addressed fairness, diversity, and transparency, these effo...

With the rising popularity of large language models (LLMs) for various applications ranging from every-day information seeking to high stakes applications such as complex medical question answering, hallucinated content poses a serious risk. Retrieval-Augmented Generation (RAG) aims to enhance the t...

Recommender systems in recruitment platforms involve two active sides, candidates and recruiters, each with distinct goals and preferences. Most existing methods address only one side of the problem, leading to potential inefficient matches. We propose a two-sided fusion framework that jointly model...

News recommender systems are increasingly driven by black-box models, offering little transparency for editorial decision-making. In this work, we introduce a transparent recommender system that uses fuzzy neural networks to learn human-readable rules from behavioral data for predicting article clic...

News recommender systems are increasingly driven by black-box models, offering little transparency for editorial decision-making. In this work, we introduce a transparent recommender system that uses fuzzy neural networks to learn human-readable rules from behavioral datafor predicting article click...

Generative search engines are reshaping information access by replacing traditional ranked lists with synthesized answers and references. In parallel, with the growth of Web3 platforms, incentive-driven creator ecosystems have become an essential part of how projects build visibility and community b...

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...

This work bridges information retrieval and cultural analytics to support equitable access to historical knowledge. Using the British Library¡¯s BL19 digital collection (more than $35,000$ works from $1700-1899$), we construct a benchmark for studying language change and retrieval in the 19th-cent...

Music recommender systems shape how people discover music, yet persistent concerns have been raised regarding fairness and representation. Achieving fairness in recommender systems is challenging because conventional methods rely on rigid quantitative criteria, making it difficult to express nuanced...

Recommender systems (RS) increasingly mediate access to crucial life spheres like employment and education, yet most remain optimized for short-term engagement rather than long-term user empowerment. While recent trustworthy RS research has addressed fairness, diversity, and transparency, these effo...

Information retrieval systems play a central role in how people access and understand information about complex social issues, including immigration. Yet little is known about how the datasets that underpin these systems represent migrants or structure public narratives about migration. In this pape...

Ensuring fairness in ranking systems is critical to avoid discriminatory outcomes towards minority groups in high stakes domains such as recruitment. Most fairness interventions only address fairness for one or more binary groups without accounting for intersectional fairness. We study the problem o...

News recommender systems are increasingly driven by black-box models, offering little transparency for editorial decision-making. In this work, we introduce a transparent recommender system that uses fuzzy neural networks to learn human-readable rules from behavioral data for predicting article clic...

Large language models (LLMs) are reshaping automated fact-checking (AFC) by enabling unified, end-to-end verification pipelines rather than isolated components. While large proprietary models achieve strong performance, their closed weights, complexity, and high costs limit sustainability. Fine-tuni...

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...

This paper presents an inference-time method to mitigate demographic bias in CLIP-like vision¨Clanguage models through targeted neural interventions in their internal attention mechanisms. We first identify ``expert'' attention heads that encode demographic information by systematically analyzing C...

Recommender systems (RS) increasingly mediate access to crucial life spheres like employment and education, yet most remain optimized for short-term engagement rather than long-term user empowerment. While recent trustworthy RS research has addressed fairness, diversity, and transparency, these effo...

Recommender systems (RS) increasingly mediate access to crucial life spheres like employment and education, yet most remain optimized for short-term engagement rather than long-term user empowerment. While recent trustworthy RS research has addressed fairness, diversity, andtransparency, these effor...

In high-stakes domains such as health and biology, information retrieval systems must ensure accuracy while also supporting equitable access and protecting sensitive data. However, many state-of-the-art biomedical IR solutions rely on proprietary cloud infrastructures, raising concerns over cost, re...

With the incredible advancements in Large Language Models (LLMs), many people have started using them to satisfy their information needs. However, utilizing LLMs might be problematic for political issues where disagreement is common and model outputs may reflect training-data biases or deliberate al...

News recommender systems are increasingly driven by black-box models, offering little transparency for editorial decision-making. In this work, we introduce a transparent recommender system that uses fuzzy neural networks to learn human-readable rules from behavioral data for predicting article clic...

Machine Translation (MT) plays a pivotal role in cross-lingual information access, public policy communication, and equitable knowledge dissemination. However, critical meaning errors, such as factual distortions, intent reversals, or biased translations, can undermine the reliability, fairness, and...