Personalized Recommendation of Dish and Restaurant Collections on iFood
Fernando F. Granado, Davi A. Bezerra, Iuri Queiroz, Nathan Oliveira, Pedro Fernandes, Bruno Schock

TL;DR
This paper introduces RED, a personalized recommendation system for curated food collections on iFood, utilizing machine learning and content-based methods to improve user engagement and overcome cold-start and bias challenges.
Contribution
The paper presents a novel large-scale recommendation system for curated food collections, integrating content-based representations and bias mitigation techniques for the first time in a commercial setting.
Findings
97% improvement in Card Conversion Rate
1.4% increase in overall App Conversion Rate
Offline metrics strongly correlate with online performance
Abstract
Food delivery platforms face the challenge of helping users navigate vast catalogs of restaurants and dishes to find meals they truly enjoy. This paper presents RED, an automated recommendation system designed for iFood, Latin America's largest on-demand food delivery platform, to personalize the selection of curated food collections displayed to millions of users. Our approach employs a LightGBM classifier that scores collections based on three feature groups: collection characteristics, user-collection similarity, and contextual information. To address the cold-start problem of recommending newly created collections, we develop content-based representations using item embeddings and implement monotonicity constraints to improve generalization. We tackle data scarcity by bootstrapping from category carousel interactions and address visibility bias through unbiased sampling of…
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