{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-cluster-representatives-for","title":"Learning Cluster Representatives for Approximate Nearest Neighbor Search","arxiv_id":"2412.05921","date":"2024-12-08","proceeding":null,"authors":["Thomas Vecchiato"],"abstract":"Developing increasingly efficient and accurate algorithms for approximate nearest neighbor search is a paramount goal in modern information retrieval. A primary approach to addressing this question is clustering, which involves partitioning the dataset into distinct groups, with each group characterized by a representative data point. By this method, retrieving the top-k data points for a query requires identifying the most relevant clusters based on their representatives -- a routing step -- and then conducting a nearest neighbor search within these clusters only, drastically reducing the search space. The objective of this thesis is not only to provide a comprehensive explanation of clustering-based approximate nearest neighbor search but also to introduce and delve into every aspect of our novel state-of-the-art method, which originated from a natural observation: The routing function solves a ranking problem, making the function amenable to learning-to-rank. The development of this intuition and applying it to maximum inner product search has led us to demonstrate that learning cluster representatives using a simple linear function significantly boosts the accuracy of clustering-based approximate nearest neighbor search.","url_abs":"https://arxiv.org/abs/2412.05921v1","url_pdf":"https://arxiv.org/pdf/2412.05921v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-cluster-representatives-for","repo_url":"https://github.com/tomvek/mips-learnt-ivf","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2412.05921","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}