128 citations · 523 across the 53 of their papers we have counts for
36 papers · 1 filter
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…
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…
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…
Towards Empathetic Conversational Recommender Systems
Xiaoyu Zhang, Ruobing Xie, Yougang Lyu +7
Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language m…
Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ens…
Robust Information Retrieval
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +1
Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only d…
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?
Ming Li, Yuanna Liu, Sami Jullien +4
Next basket recommendation (NBR) is a special type of sequential recommendation that is increasingly receiving attention. So far, most NBR studies have focused on optimizing the ac…
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
Jin Huang, Harrie Oosterhuis, Masoud Mansoury +2
Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of i…