[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124938-en":3,"doc-seo-124938-105":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},124938,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","iFood Catalog Enhancement - Improving Restaurant Catalog Management and User Recommendations at iFood using Machine Learning and Search Techniques","This thesis presents an in-depth exploration and implementation of advanced Data Science methodologies within the operational context of iFood, a prominent Brazilian online food delivery platform. The study enhances the management and recommendation processes for items in iFood’s extensive restaurant catalog, enabling informed, data-driven decisions. It focuses on machine learning models that tackle the lack of standardized, centralized catalog information caused by diverse, unstructured restaurant inputs through taxonomy classification, guided by modularity, accuracy, cost-effectiveness, and MECE. Continuous monitoring addresses data drift and integrates Human-in-the-Loop for real-time error correction and validation, using Logistic Regression, XGBoost, FoodBERT, and TF-IDF.","Master Degree Program in  \nData Science and Advanced Analytics  \nMDSAA  \niFood Catalog Enhancement  \nImproving Restaurant Catalog Management and User Recommendations at iFood using Machine Learning Techniques  \nAntónio Afonso Silva Pinto  \nInternship Report  \npresented as partial requirement for obtaining a Master’s Degree in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \niFood Catalog Enhancement  \nImproving Restaurant Catalog Management and User Recommendations at iFood using  \nMachine Learning and Search Techniques  \nby  \nAntónio Afonso Silva Pinto  \nInternship Report presented as partial requirement for obtaining the Master’s degree in Data Science and Advanced Analytics, with a specialization in Data Science  \nSupervised by  \nProf. Mauro Castelli, PhD  \nNovember, 2023  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nAntónio Pinto  \nLisbon, September 2023  \nACKNOWLEDGEMENTS  \nI would like to acknowledge my advisor Mauro Castelli, whose passion for machine learning and dedication to teaching have been a constant source of inspiration, propelling me to exceed expectations.  \nSpecial thanks are due to the exceptional mentorship provided by Murilo Menezes and the data catalogue team at iFood. I am deeply appreciative of the support and collaborative spirit, and I am eagerly anticipating the achievements we will unlock together in future projects.  \nFinally, heartfelt thanks to my parents, Madalena and José, for their boundless love and unwavering motivation, serving as pillars of support throughout this journey.  \nABSTRACT  \nThis thesis presents an in-depth exploration and implementation of advanced Data Science methodologies within the operational context of iFood, a prominent Brazilian online food delivery platform. The principal objective of the study was to augment the management and recommendation processes for items within iFood's extensive restaurant catalog, fostering informed and data-driven decision-making.  \nThe focal point of this endeavor was the development and deployment of machine learning models crafted to address a critical challenge, the lack of standardized, centralized information within the platform's catalog due to the diverse and unstructured data input from restaurants. The primary emphasis lay in taxonomy classification, aiming to systematically categorize menu items into a structured framework. Guided by principles of modularity, accuracy, cost-effectiveness, and the MECE (Mutually Exclusive, Collectively Exhaustive) principle, the classification ensured a comprehensive and non-overlapping categorization of items.  \nKey to the project's success was the handling of data drift, a phenomenon where the performance of machine learning models degrades over time due to changes in input data distributions. The study emphasized the importance of continuous model monitoring and the integration of “Human in the Loop”(HITL) systems for real-time error correction and model validation. This approach ensured that the models remained accurate and relevant in the dynamic environment of the food delivery industry.  \nThe thesis employed a range of techniques including Logistic Regression, XGBoost, FoodBERT (a specialized adaptation of Google's BERT model for the food domain), and TF-IDF vectorization. The project's comprehensive nature extended beyond model accuracy, encapsulating broader business and operational considerations such as cost, interpretab","cbCaiqxO6FQEebnr","https://ap.wps.com/l/cbCaiqxO6FQEebnr","pdf",1416896,1,57,"English","en",105,"# Introduction\n## Company overview\n## Motivation and objectives\n### Taxonomy Classification\n### Feedback loop\n# Literature review\n## Tech in Food delivery\n## Text Classification\n### Fundamental concepts\n### Text preprocessing and transformations\n### Supervised Learning\n#### Regular expressions\n#### Logistic regression\n#### Ensemble models\n#### Food Bert","[{\"question\":\"What problem does the thesis address in iFood’s catalog?\",\"answer\":\"The work targets the lack of standardized, centralized information in iFood’s restaurant catalog, driven by diverse and unstructured inputs from restaurants.\"},{\"question\":\"How does the thesis structure item organization?\",\"answer\":\"It develops and deploys taxonomy classification models to systematically categorize menu items into a structured framework using MECE principles to avoid overlap.\"},{\"question\":\"How is model quality maintained over time?\",\"answer\":\"The thesis emphasizes continuous monitoring to handle data drift and integrates a Human-in-the-Loop system for real-time error correction and validation.\"}]","iFood Catalog Enhancement - Improving Restaurant Catalog Management and User Recommendations at iFood using Machine Learning and Search Techniques | PDF",1785895496,144,{"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},"ifood-catalog-enhancement-improving-restaurant-catalog-management-and-user-recommendations-at-ifood-using-machine-learning-and-search-techniques","",{"@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/ifood-catalog-enhancement-improving-restaurant-catalog-management-and-user-recommendations-at-ifood-using-machine-learning-and-search-techniques/124938/",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","2026-08-05",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 problem does the thesis address in iFood’s catalog?","Question",{"text":75,"@type":76},"The work targets the lack of standardized, centralized information in iFood’s restaurant catalog, driven by diverse and unstructured inputs from restaurants.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis structure item organization?",{"text":80,"@type":76},"It develops and deploys taxonomy classification models to systematically categorize menu items into a structured framework using MECE principles to avoid overlap.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model quality maintained over time?",{"text":84,"@type":76},"The thesis emphasizes continuous monitoring to handle data drift and integrates a Human-in-the-Loop system for real-time error correction and validation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]