Building a Full-Stack Movie Recommendation System from Scratch: A Complete Practice from Data Processing to Real-Time Deployment

This article introduces the MovieRecommenderSystem project released by ManamiMayoki on GitHub, covering the full-stack workflow from data preprocessing, machine learning model training to Web application integration and real-time deployment, providing a reference for developers who want to understand the engineering practice of recommendation systems. The project was released on 2026-05-30, original link: https://github.com/ManamiMayoki/MovieRecommenderSystem.

Recommendation systems are core components of modern digital platforms (e.g., Netflix, Taobao), providing personalized content by analyzing user behavior and item features. MovieRecommenderSystem demonstrates how to advance machine learning models from prototype to production environment, covering recommendation algorithms, data engineering, backend services, frontend interfaces, deployment and operation and maintenance, providing developers with a complete end-to-end reference.

The system adopts a layered architecture: the data layer is responsible for raw data preprocessing (cleaning, missing value handling, feature engineering); the model layer implements recommendation algorithms (training, evaluation, version management); the service layer encapsulates models into APIs, handling concurrency and caching; the presentation layer is a responsive Web application supporting user interaction. The layered design ensures maintainability and scalability.

Data preprocessing includes: 1. User-movie interaction data processing (ratings, viewing history, solving sparsity issues); 2. Movie metadata processing (encoding of genres, directors, etc., for content-based recommendation); 3. Feature engineering (building user profiles, movie popularity, temporal features, etc., to improve recommendation quality).

The project may adopt multiple recommendation technologies: collaborative filtering (user/item-based, matrix factorization such as SVD, ALS), content-based recommendation (using movie features), hybrid recommendation (combining multiple technologies to balance accuracy and diversity). For specific algorithm details, please refer to the source code.

The frontend uses responsive design, adapting to multiple devices, supporting movie browsing, rating, and receiving real-time recommendations; the backend handles requests, calls models, and manages sessions; deployment uses containerization (e.g., Docker) and cloud services to ensure high availability and elastic scaling; real-time recommendations require efficient inference and caching strategies.

Engineering practices include modular design (facilitating testing and maintenance), performance optimization (data pipelines, model acceleration, caching); evaluation metrics cover accuracy, recall, coverage, etc., supporting offline evaluation and online A/B testing; strategies such as popular recommendations and initial profiling are used to address cold start issues (new users/movies).

The architecture can be adapted to recommendation scenarios such as movies, music, books, etc.; expansion directions include introducing deep learning, real-time feature updates, explainable recommendations, multi-objective optimization; it is recommended that developers start with data flow, gradually dive into model implementation, focus on deployment optimization, and the project code is an excellent learning resource.