The article outlines how to create a functional food‑delivery system for a graduation project by focusing on a core transaction loop, detailing user, merchant, and admin modules, and explaining why the rider side is intentionally limited to keep the project manageable.
This article dissects the architecture of a high‑traffic food‑delivery platform, covering layered micro‑service design, core service implementations, smart dispatch algorithms, real‑time rider tracking, peak‑time scaling strategies, and practical solutions to common performance and reliability challenges.
Meituan’s food‑delivery team built a novelty‑focused recommendation pipeline—combining dual‑tower recall, novelty‑aware ranking, personalized mixed‑ranking weights, and reinforcement‑learning insertion—to surface merchants unseen by users, achieving 19% higher exposure novelty, 25% more order novelty, and improved ratings while keeping RPM loss under 0.5%.
Meituan’s delivery team created an intelligent, real‑time companion‑type search guidance system for its waimai platform—combining a smart in‑box word refresh triggered by edge‑intelligence intent signals and a unified multi‑scenario query‑recommendation model with self‑supervised pre‑training and multi‑objective optimization—delivering over 1 % DAU growth, 1.7 % UV_RPM increase, and up to 187 % CTR lifts while outlining future extensions to more pages and large‑model embeddings.
This article details Meituan's interactive recommendation system for its food‑delivery homepage feed, covering the motivation, challenges, system architecture, user intent modeling, evaluation metrics, experimental results, and future directions, illustrating how real‑time, user‑centric recommendations improve conversion and user experience.
This article details Meituan Waimai's end‑to‑end interactive recommendation system for the food‑delivery homepage feed, explaining its architecture, trigger strategies, recall and ranking pipelines, evaluation metrics, experimental results, and future optimization directions.
This article presents a complete food delivery system project built with Spring Boot and Vue.js, detailing its technology stack, modules, database setup, and step‑by‑step instructions for launching both the admin backend and mobile client, along with resource download links.
This article introduces a full‑stack food‑delivery project built with Spring Boot, Vue.js, MySQL and MongoDB, detailing its architecture, module breakdown, technology choices, database setup, and step‑by‑step instructions for launching the backend API, admin portal, and mobile client, along with download links for data and images.
This tutorial walks you through creating a full‑stack food‑delivery application using Spring Boot for the backend, Vue.js for the frontend, MySQL and MongoDB for data storage, and provides detailed setup commands, module descriptions, and launch instructions.
Chinese internet companies like Meituan, Weibo, and Didi are increasingly revealing the logic behind their core algorithms—such as order allocation and hot‑search ranking—to address public concerns, comply with new privacy laws, and balance commercial secrecy with societal trust.
This article introduces the Flash-Waimai project—a full‑stack food delivery system built with Spring Boot, Vue.js, MySQL and MongoDB—detailing its architecture, module breakdown, database setup, quick‑start commands, and step‑by‑step instructions for launching the backend, admin portal, and mobile client.
The paper outlines an end‑to‑end pipeline that standardizes highly personalized food‑delivery dish names by combining rule‑based and BERT‑DSSM text synonym detection with EfficientNet image classification, constructing a multi‑level taxonomy that improves aggregation, supply‑demand analysis, recall ranking and merchant tagging.
This article provides a complete tutorial for building a food‑delivery system with a mobile app, backend management, and API, detailing the technology stack, module structure, database setup, code snippets, and step‑by‑step instructions to launch both the admin and mobile front‑ends.
This article presents a complete food‑delivery system with mobile, admin, and API modules, detailing its Spring Boot and Vue.js technology stack, module structure, database setup, quick‑start commands, and how to run both the management console and mobile client for learning purposes.
Flash‑Waimai is an open‑source, full‑stack food delivery application featuring a mobile client, admin portal, and API, built with Spring Boot for the backend, Vue.js for the frontend, MySQL and MongoDB for data storage, and includes detailed setup instructions, code snippets, and deployment guides.
Alibaba’s local life smart logistics team presents a KDD 2020 oral paper detailing a deep‑learning model for order fulfillment cycle time (OFCT) estimation in on‑demand food delivery, describing feature engineering, rider encoding, post‑processing operators, and experimental results that significantly improve prediction accuracy and user experience.
By training Gradient Boosted Decision Trees on offline delivery data, extracting leaf‑node indices as one‑hot features, and merging them with merchant, traffic, and weather information, the study reduces overall ETA MAE by 3.4%, raises N‑minute accuracy by 2.2 points, and achieves larger gains for high‑value orders, demonstrating that GBDT‑derived features markedly improve food‑delivery time predictions.
The article describes a data‑driven approach for identifying low‑value new users in a food‑delivery platform by labeling 7‑day repeat‑purchase behavior, extracting order, behavior, merchant and user features, and training a combined Gradient Boosted Decision Tree and Logistic Regression model to improve fraud detection and merchant penalty decisions.
This article describes a cloud‑based intelligent logistics scheduling platform for food‑delivery services that leverages big‑data analytics, deep‑learning prediction models, and visualisation tools to achieve multi‑objective dynamic optimization, improve dispatch efficiency, and enhance user experience across thousands of cities.
