128 citations · 523 across the 53 of their papers we have counts for
9 papers · 1 filter
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…