M. de Rijke

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

Prompt Learning for Textual Heterogeneous Information Networks

Heterogeneous information networks (HINs) play an indispensable role in a wide range of domain-specific applications, from recommender systems to conversational platforms. Textual heterogeneous information networks (HINs) are graphs with abundant textual information. Currently, most advanced approaches to mine textual...

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...