About us
Information drives the planet. We organize talks around implementations of information retrieval, in search engines, in recommender systems, or in conversational assistants. Our meetups are usually held on the last Friday of the month, at Science Park Amsterdam. Usually, we have two talks in a row, one industrial, the other academic, 25+5 minutes each, no marketing, just algorithms, followed by drinks. We also host ad hoc "single shot" events whenever an interesting visitor stops and shares their work.
Search Engines Amsterdam is supported by the ELLIS unit Amsterdam.
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Upcoming events
12
SEA: Search Engines Amsterdam
Amsterdam Science Park 904, Science Park 904, Amsterdam, NLThis session focuses on Sparse and Ranking in Modern Retrieval, featuring two speakers: Simon Lupart from University of Amsterdam and Chuan Meng from University of Edinburgh.
Location: Science Park 904, Room C3.161
Date: Friday, April 24
Time: 16:00-17:00
Zoom link: https://uva-live.zoom.us/j/65011610507Details below:
Speaker #1: Simon Lupart from University of Amsterdam
Title: T.B.D
Abstract: T.B.D
Bio: T.B.DSpeaker #2: Chuan Meng from University of Edinburgh
Title: Revisiting Text Ranking in Deep Research
Abstract: Deep research has emerged as an important task that aims to address hard queries through extensive open-web exploration. To tackle it, most prior work equips large language model (LLM)-based agents with opaque web search APIs, enabling agents to iteratively issue search queries, retrieve external evidence, and reason over it. Despite search's essential role in deep research, black-box web search APIs hinder systematic analysis of search components, leaving the behaviour of established text ranking methods in deep research largely unexplored. In this talk, we revisit key findings and best practices for text ranking methods in the deep research setting. In particular, we examine their effectiveness from three perspectives: (i) retrieval units (documents vs. passages), (ii) pipeline configurations (different retrievers, re-rankers, and re-ranking depths), and (iii) query characteristics (the mismatch between agent-issued queries and the training queries of text rankers). We perform experiments on BrowseComp-Plus, a recent deep research dataset with a fixed document corpus, evaluating a broad spectrum of text ranking methods across diverse setups. We find that agent-issued queries typically follow web-search-style syntax (e.g., quoted exact matches), favouring lexical, learned sparse, and multi-vector retrievers. Passage-level units are more efficient under limited context windows, and avoid the difficulties of document length normalisation in lexical retrieval. Re-ranking consistently improves performance, with deeper re-ranking depths amplifying gains. Translating agent-issued queries into natural-language questions significantly bridges the query mismatch and improves effectiveness.
Related paper: https://arxiv.org/pdf/2602.21456
Bio: Chuan Meng is a postdoc at the University of Edinburgh, working with Dr. Jeff Dalton. He received his PhD from the University of Amsterdam in June 2025, supervised by Prof. Maarten de Rijke and Dr. Mohammad Aliannejadi. He was formerly an Applied Scientist Intern at Amazon. His research focuses on agentic information retrieval (IR) and deep research. He has published 25+ papers in top-tier venues such as SIGIR, ACL, EMNLP, NAACL, CIKM, AAAI, ECIR, and TOIS, with 650+ citations on Google Scholar and an h-index of 16. He has co-organised tutorials and workshops at SIGIR 2025, WSDM 2025, ECIR 2024–2026, SIGIR-AP 2024.
Counter: SEA Talks #303 and #304.
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