Recommender Systems in Delivery Platforms: Challenges, Solutions and Learnings

Abstract

Delivery platforms introduce unique challenges for recommender systems, as they must optimize a digital experience under physical and operational constraints. Items are tied to specific locations with delivery radii, real-time inventory, and preparation times - signals absent in other recommendation domains. More fundamentally, these platforms are multi-sided marketplaces where the interests of consumers, merchants, couriers, and the platform itself are often part of large multi-objective optimization. Recommendations must navigate these competing objectives at scale and incorporate feedback that is both delayed and heterogeneous, extending beyond standard engagement signals to fulfillment outcomes such as wait times and item availability. This tutorial provides a practical overview of how recommendation systems are designed under these constraints. We cover the full surface landscape - from homepage discovery to item-level ranking - and address challenges including cold-start in a three-sided market, the glocal problem of globally trained models under hyper-local delivery constraints, and the alignment of offline metrics (MRR, NDCG, Recall) to online and business outcomes (conversion, gross order value, and retention). We present production recommender systems spanning the full recommendation stack, from representation learning and retrieval to ranking, page optimization, and generative methods, across both store- and item-level surfaces. We cover cross-domain store ranking across restaurants, grocery, and retail, as well as approaches for item ranking and product hierarchy structuring. Delivered by practitioners from Wolt and DoorDash, operating at scale in 40+ countries, the tutorial provides a system- and model-level understanding of recommender systems in delivery marketplaces.