128 citations · 523 across the 53 of their papers we have counts for
54 papers
Do We Care About Personalization and Explainability? An Interview Study with News Recommendation Engineers
Jasmin Kareem, Siddharth Mehrotra, Martijn C. Willemsen +1
Research on explainability in recommender systems largely centers on end users, overlooking the perspectives of those who build and maintain these systems and their potential use c…
Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?
Daniel Gomm, Maarten de Rijke, Madelon Hulsebos
Democratizing access to the knowledge held in large corpora of tables such as data lakes is emerging as a central research challenge. Research in this space is advancing and broade…
An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark
Peibo Li, Yang Song, Hao Xue +2
Cross-city point-of-interest (POI) recommendation is crucial for navigating unfamiliar urban environments, yet its progress has historically been constrained by data limitations. U…
DeepRepro: State-Aware Subplanning for Paper-to-Code Reproduction in Evolving Repositories
Hongru Song, Ruqing Zhang, Jiafeng Guo +2
Recent advances in agentic large language models (LLMs) have enabled increasingly autonomous software engineering workflows, yet automatic machine learning (ML) paper-to-code repro…
Closing the Indexing-Decoding Gap in Multimodal Generative Retrieval via Prefix Retention Optimization
Yufei Chen, Zihan Wang, Yubao Tang +3
Multimodal generative retrieval formulates multimodal retrieval as discrete identifier generation, eliminating the need for explicit similarity search over external embeddings. Exi…
The Multilingual Curse at the Retrieval Layer: Evidence from Amharic
Yosef Worku Alemneh, Kidist Amde Mekonnen, Maarten de Rijke
Multilingual retrieval increasingly underpins cross-lingual question answering and retrieval-augmented generation. Strong zero-shot scores on multilingual benchmarks are often take…
The Attention Market: Interpreting Online Fair Re-ranking as Manifold Optimization under Walrasian Equilibrium
Chen Xu, Wei Chu, Wenyu Hu +3
Fair re-ranking aims to promote long-tail items and enhance diversity within groups in information retrieval. While previous research on online fairness-aware re-ranking has shown…
A Parametric Memory Head for Continual Generative Retrieval
Kidist Amde Mekonnen, Yubao Tang, Maarten de Rijke
Generative information retrieval (GenIR) consolidates retrieval into a single neural model that decodes document identifiers (docids) directly from queries. While this model-as-ind…
AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning
Hongru Song, Yu-An Liu, Ruqing Zhang +4
Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack surfaces. We study knowledge-base…
ERASE -- A Real-World Aligned Benchmark for Unlearning in Recommender Systems
Pierre Lubitzsch, Maarten de Rijke, Sebastian Schelter
Machine unlearning (MU) enables the removal of selected training data from trained models, to address privacy compliance, security, and liability issues in recommender systems. Exi…
Do Images Clarify? A Study on the Effect of Images on Clarifying Questions in Conversational Search
Clemencia Siro, Zahra Abbasiantaeb, Yifei Yuan +2
Conversational search systems increasingly employ clarifying questions to refine user queries and improve the search experience. Previous studies have demonstrated the usefulness o…
Tutorial on Reasoning for IR & IR for Reasoning
Mohanna Hoveyda, Panagiotis Efstratiadis, Arjen de Vries +1
Information retrieval has long focused on ranking documents by semantic relatedness. Yet many real-world information needs demand more: enforcement of logical constraints, multi-st…
OrLog: Resolving Complex Queries with LLMs and Probabilistic Reasoning
Mohanna Hoveyda, Jelle Piepenbrock, Arjen P de Vries +2
Resolving complex information needs that come with multiple constraints should consider enforcing the logical operators encoded in the query (i.e., conjunction, disjunction, negati…
Unveiling and Simulating Short-Video Addiction Behaviors via Economic Addiction Theory
Chen Xu, Zhipeng Yi, Ruizi Wang +3
Short-video applications have attracted substantial user traffic. However, these platforms also foster problematic usage patterns, commonly referred to as short-video addiction, wh…
Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank (Extended Abstract)
Philipp Hager, Onno Zoeter, Maarten de Rijke
Additive two-tower models are popular learning-to-rank methods for handling biased user feedback in industry settings. Recent studies, however, report a concerning phenomenon: trai…
Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank
Philipp Hager, Onno Zoeter, Maarten de Rijke
Additive two-tower models are popular learning-to-rank methods for handling biased user feedback in industry settings. Recent studies, however, report a concerning phenomenon: trai…
Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation
Peibo Li, Shuang Ao, Hao Xue +5
Advancing large language models (LLMs) for the next point-of-interest (POI) recommendation task faces two fundamental challenges: (i) although existing methods produce semantic IDs…
An Empirical Study of Evaluating Long-form Question Answering
Ning Xian, Yixing Fan, Ruqing Zhang +2
\Ac{LFQA} aims to generate lengthy answers to complex questions. This scenario presents great flexibility as well as significant challenges for evaluation. Most evaluations rely on…
Replication and Exploration of Generative Retrieval over Dynamic Corpora
Zhen Zhang, Xinyu Ma, Weiwei Sun +6
Generative retrieval (GR) has emerged as a promising paradigm in information retrieval (IR). However, most existing GR models are developed and evaluated using a static document co…
Adaptive Orchestration of Modular Generative Information Access Systems
Mohanna Hoveyda, Harrie Oosterhuis, Arjen P. de Vries +2
Advancements in large language models (LLMs) have driven the emergence of complex new systems to provide access to information, that we will collectively refer to as modular genera…
Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Kidist Amde Mekonnen, Yubao Tang, Maarten de Rijke
Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task, allowing for…
Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics
Chen Xu, Jujia Zhao, Wenjie Wang +4
Fairness is an increasingly important factor in re-ranking tasks. Prior work has identified a trade-off between ranking accuracy and item fairness. However, the underlying mechanis…
A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition
Zihan Wang, Ziqi Zhao, Yougang Lyu +3
Zero-shot named entity recognition (NER) aims to develop entity recognition systems from unannotated text corpora. This task presents substantial challenges due to minimal human in…
AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit
Mohanna Hoveyda, Arjen P. de Vries, Maarten de Rijke +2
In question answering (QA), different questions can be effectively addressed with different answering strategies. Some require a simple lookup, while others need complex, multi-ste…