M. de Rijke

Does Target Class Matter? A Reproducibility Study of Plausible Counterfactual Explanations for Image Classification

Do We Care About Personalization and Explainability? An Interview Study with News Recommendation Engineers

This study examines how news engineers and related technical stakeholders perceive and implement personalization and explainability in practice, and provides actionable and practical guidelines for news engineers and researchers on how to adopt explainability methods within a news personalization pipeline.

Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?

This work formalizes OpenTI from first principles around the analytical knowledge a person needs, the procedure for deriving it from a corpus of tables, and how well a result serves the person who sought it, and distill a research agenda towards OpenTI systems, evaluation, and interaction paradigms that surface the ins...

Prompt Learning for Textual Heterogeneous Information Networks

This work is among the first to introduce and exploit the idea of prompt learning to align HIN features and textual features, and is composed of a text encoder and a graph encoder and uses contrastive learning to align and fuse the graph–text pair.

UvA-DARE (

This work studies the repetition problem for encoder-decoder models, using both recurrent neural network (RNN) and trans-former architectures and proposes token loss dynamic reweighting (TLDR) that applies differentiable weights to individual token losses.

UvA-DARE (

This work studies the repetition problem for encoder-decoder models, using both recurrent neural network (RNN) and trans-former architectures and proposes token loss dynamic reweighting (TLDR) that applies differentiable weights to individual token losses.

Reasoning for IR & IR for Reasoning

This tutorial first articulates a working definition of reasoning within the context of information retrieval and derives from it a unified analytical framework, which maps existing approaches along axes that reflect the core components of the definition.

Reward Shaping for Robust Refusal in Small Language Models for Retrieval-Augmented Question Answering

It is shown that instruction-tuned models generate answers even when explicitly prompted to refuse when the answer is not supported by the documents, and Reward Shaping for Refusal and Reasoning (RSRR), a reinforcement learning framework that teaches LMs to reason step-by-step over multiple documents, is introduced.

DeepRepro: State-Aware Subplanning for Paper-to-Code Reproduction in Evolving Repositories

DeepRepro dynamically transforms evolving repository states and runtime feedback into fine-grained implementation subplans, keeping planning aligned with execution throughout repository construction, and consistently outperforms strong scientific and commercial code-agent baselines.

An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark

The need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories is highlighted, with results highlighting the need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reason...