[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119607-en":3,"doc-seo-119607-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119607,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","A Machine Learning Personalization Flow","A machine learning personalization flow explores how to model user preferences and translate interaction signals into dynamic, tailored recommendations. The thesis develops and evaluates methods aimed at improving personalization quality while addressing practical constraints found in real-world data and iterative training. It focuses on rigorous research activities at the Information Retrieval Lab, supported by industry partners, and is presented as an academic doctoral work. Results and discussions connect algorithmic choices, evaluation practices, and the implications for post-PhD research directions.","UvA-DARE (Digital Academic Repository)  \nA machine learning personalization flow  \nBénédict, G.  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nLink to publication  \nCitation for published version (APA):  \nBénédict, G. (2024) . A machine learning personalization flow. [Thesis, fully internal, Universiteit van Amsterdam] .  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:05 Jan 2026  \nA Machine Learning Personalization Flow  \nGabriel B􀀓en􀀓edict  \nA Machine Learning Personalization Flow  \nAcademisch Proefschrift  \nter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus [prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek)  \nten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen inde Aula der Universiteitop vrijdag 8 maart 2024, te 11.00 uur  \ndoor  \nGabriel Bénédict  \ngeboren te Chavannes-Près-Renens  \nPromotiecommissie  \nPromotor: prof. dr. M. de Rijke  \nCo-promotor: dr. D. Odijk  \nOverige leden: prof. dr. K. Balog prof. dr. N. Helberger dr. M. Lalmas  \ndr. C.A. Naesseth  \nprof. dr. M. Worring  \nUniversiteit van Amsterdam RTL NL  \nUniversity of Stavanger Universiteit van Amsterdam Spotify  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nFaculteit der Natuurwetenschappen, Wiskunde en Informatica  \nThe research was carried out at the Information Retrieval Lab at the University of Amsterdam, with support from RTL NL & Bertelsmann SE & Co. KGaA.  \nCopyright © 2024 Gabriel Bénédict, Amsterdam, The Netherlands Printed by Proefschriftspecialist, Zaandam  \nISBN: 978-94-93330-58-0  \nCertains pensent qu’ils font un voyage, en fait, c’est le voyage qui vous fait ou  \nvous défait.  \n– Nicolas Bouvier  \nAcknowlegements  \nThe concept of an industry PhD was foreign to me, but it turned out to bea great experience. I joined during COVID, while the lab was still temporarily located in containers. From there on, conditions only improved. We moved on to the beautiful offices at Lab42 that felt more like a tech company than a university building. These great working conditions, combined with colleagues always pushing for a good work-life balance contributed to a great PhD experience.  \nMy supervisors were the central piece in making this a smooth experience. Daan: thank you for giving me so many liberties at RTL and always having our team’s back. Maarten: thank you for always asking questions, and encouraging me to do more creative things like organize a workshop!  \nKrisztian Balog, Natali Helberger, Mounia Lalmas, Christian Andersson Naesseth and Marcel Worring, thank you for agreeing to be part of my PhD committee, and for your valuable time to read and discuss my thesis. Mounia: thank you for listening to my research ideas, from early on as you visited our labin the container phase. Our later discussions were then essential in orienting my post PhD career. Natali: thank you for supporting the inter-faculty collaborat","cbCaihxMg5zPZuTx","https://ap.wps.com/l/cbCaihxMg5zPZuTx","pdf",12938440,1,178,"English","en",105,"# Acknowlegements\n## Industry PhD experience and lab environment\n## Supervisors and PhD committee\n## Research, collaboration, and community support","[{\"question\":\"What is the main topic of the thesis?\",\"answer\":\"The thesis focuses on a machine learning personalization flow, aiming to turn user-related signals into tailored personalization behavior.\"},{\"question\":\"Where was the research conducted?\",\"answer\":\"The research was carried out at the Information Retrieval Lab at the University of Amsterdam, with support from RTL NL and Bertelsmann SE \\u0026 Co. KGaA.\"},{\"question\":\"What does the document include besides the research content?\",\"answer\":\"It includes an acknowledgements section that credits supervisors, PhD committee members, lab colleagues, and contributions to collaboration and writing.\"}]","A Machine Learning Personalization Flow | PDF",1785725271,449,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-personalization-flow","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-personalization-flow/119607/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main topic of the thesis?","Question",{"text":75,"@type":76},"The thesis focuses on a machine learning personalization flow, aiming to turn user-related signals into tailored personalization behavior.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Where was the research conducted?",{"text":80,"@type":76},"The research was carried out at the Information Retrieval Lab at the University of Amsterdam, with support from RTL NL and Bertelsmann SE & Co. 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