Modul praktis Level 2 VibeKoding: Proyek: Sistem Rekomendasi Film Enterprise (Spring Boot).Modul praktis Level 2 VibeKoding: Proyek: Sistem Rekomendasi Film Enterprise (Spring Boot).
This project requires you to build a movie website with recommendation capabilities using Spring Boot, based on a real PRD. The core challenge is not simple CRUD — it's thinking about "how user behavior affects recommendations" and "how to make recommendations explainable."This project requires you to build a movie website with recommendation capabilities using Spring Boot, based on a real PRD. The core challenge is not simple CRUD — it's thinking about "how user behavior affects recommendations" and "how to make recommendations explainable."
This is the comprehensive practical section of Stage 2. You'll encounter the "content + behavior + recommendation" product development pattern for the first time, which is common in e-commerce, content platforms, and personalized feeds.This is the comprehensive practical section of Stage 2. You'll encounter the "content + behavior + recommendation" product development pattern for the first time, which is common in e-commerce, content platforms, and personalized feeds.
Before starting this project, you should already be familiar with:Before starting this project, you should already be familiar with:
After completing this project, you will be able to:After completing this project, you will be able to:
You will build a movie website with recommendation capabilities:You will build a movie website with recommendation capabilities:
| Feature | Description |
|---|---|
| Browse & Search | Users can browse and search for movies |
| Ratings & Favorites | Users can rate and favorite movies |
| Personalized Recommendations | The system generates recommendations based on user behavior |
| Admin Dashboard | Admins manage movie data and view recommendation performance |
The requirements document for this project is on GitHub: [View PRD](https://github.com/datawhalechina/easy-vibe/blob/main/docs/en/stage-2/assignments/movie-recommendation-springboot/PRD.md)The requirements document for this project is on GitHub: [View PRD](https://github.com/datawhalechina/easy-vibe/blob/main/docs/en/stage-2/assignments/movie-recommendation-springboot/PRD.md)
{ title: 'Scaffold', description: 'Use AI to generate list, detail, recommendation, and admin pages' },{ title: 'Scaffold', description: 'Use AI to generate list, detail, recommendation, and admin pages' }, { title: 'Iterate', description: 'Add recommendation logic, behavior tracking, and admin management' },{ title: 'Iterate', description: 'Add recommendation logic, behavior tracking, and admin management' }, { title: 'Launch', description: 'End-to-end testing, deploy, and prepare demo' }{ title: 'Launch', description: 'End-to-end testing, deploy, and prepare demo' } ]" />]" />
Open the PRD document and answer these key questions:Open the PRD document and answer these key questions:
If the above questions don't have clear answers, don't start coding. Unclear requirements are the most common cause of rework.If the above questions don't have clear answers, don't start coding. Unclear requirements are the most common cause of rework.
mermaid flowchart TD prd["PRD"] --> web["Frontend Pages"] web --> auth["User Auth"] web --> movie["Movie List / Details"] web --> behavior["Rating / Favorite"] behavior --> reco["Recommendation Logic"] reco --> db["Database"] admin["Admin Dashboard"] --> db
Prompt reference:Prompt reference:
text Based on the current PRD, help me generate a frontend scaffold for a Spring Boot movie recommendation system. Requirements: 1. Pages: homepage, movie list, movie detail, recommendation page, user profile, admin dashboard 2. Only generate page structure with mock data first, no real API integration 3. Style should look like a real content product, not a classroom demo
Check each item:Check each item:
| Check Item | Verification Method |
|---|---|
| Basic features | Is list, detail, rating, favorite a closed loop? |
| Recommendation linkage | Does user behavior affect recommendation results? |
| Explainability | Can users understand why these movies were recommended? |
| Admin data | Can admins view movie data and recommendation performance? |
At minimum, verify these scenarios:At minimum, verify these scenarios:
After completing this project, submit the following:After completing this project, submit the following:
| Dimension | Basic Requirements | Advanced Requirements |
|---|---|---|
| PRD Alignment | Pages, features, and data structures basically match PRD | Can clearly explain design decisions |
| Product Loop | Browse → Rate → Favorite → Recommend works end-to-end | Rating behavior visibly affects recommendations |
| Recommendation Quality | Results are reasonable, reasons are explainable | Supports multiple recommendation strategies |
| Admin Capability | Movie data and recommendation performance viewable | Has stats like recommendation accuracy metrics |
| Engineering Completeness | Frontend, Spring Boot backend, database pipeline connected | Recommendation API has caching or performance optimization |