practical-recommender-systems/moviegeek

<explanation>

1. Identify the project type and purpose

- This is a full‑stack Django web application (“MovieGEEK”) whose primary purpose is to demonstrate and serve a variety of recommender‑system algorithms. It collects movie and rating data, builds recommendation models, evaluates them, and presents analytics and recommendations via a web UI.

2. Analyze the file structure

- Top‐level Django project: prs_project/ (settings, WSGI, URLs) and manage.py.

- Django apps:

• collector/ – endpoints and models for ingesting movie/rating data

• builder/ – batch calculators that build recommendation models (e.g., matrix factorization, item similarity, LDA)

• evaluator/ – code to run algorithmic evaluations and coverage/precision metrics

• recommender/ – Django views/models for serving recommendations at runtime

• analytics/ – views/templates for visualizing model outputs (clusters, similarity graphs, LDA topics, user/item analytics)

• moviegeeks/ – core app for browsing/searching movies, detail pages

- Support code:

• recs/ – reusable recommender classes (BPR, FunkSVD, popularity, content-based, neighborhood)

• populate_*.py scripts & db-migrate.sh – database setup and data loading

• notebooks/ – exploratory analyses

- Configuration: Dockerfile, docker-compose.yml, .travis.yml, requirements.txt, prs_project/settings.py.

3. Additional insights from README

- Uses SQLite (or PostgreSQL) for the movie/rating database.

- External dependency on “themoviedb.org” API for movie posters.

- Supports both Dockerized deployment and virtualenv.

- Batch pipeline: build models → migrate schemas → populate data → evaluate algorithms → serve via web.

4. How to draw the system design diagram

a. Main components:

• Client (browser)

• Django Web Server (Docker container)

• Django Apps:

– moviegeeks (UI for browsing/search)

– collector (data ingestion REST endpoints)

– recommender (online recommendation API)

– analytics (visualization dashboards)

• Batch Pipeline (outside/inside container):

– builder (model calculators)

– evaluator (evaluation runner)

• Recommender Library (recs/)

• Database (SQLite or Postgres)

• External Service: themoviedb.org API

b. Relationships and interactions:

1. User → HTTP request to Web Server → appropriate Django app (moviegeeks, analytics, recommender).

2. collector app can be called to ingest new data → writes to DB.

3. db-migrate.sh → orchestrates manage.py makemigrations/migrate and populate_*.py → populates DB.

4. builder reads raw data from DB → runs algorithms via builder/*.py using recs/ library → persists model artifacts (in DB or file store).

5. evaluator reads these models and data → computes metrics → may write results to DB (for analytics).

6. analytics app reads evaluation/model results → renders dashboards.

7. recommender app uses model artifacts + recs/ classes to serve real‑time recommendations.

8. Web Server fetches poster images from themoviedb.org on the fly.

c. Architectural patterns and design principles:

- Separation of concerns: apps for ingestion, modeling, evaluation, serving, UI

- Modular recommender library for algorithm reuse

- Batch vs. real‑time layers: builder/evaluator (offline) vs. recommender (online)

- Layered architecture: Presentation → Business Logic (Django apps) → Data Access → External APIs

d. Key technologies and frameworks:

- Python 3, Django framework

- Docker & docker‑compose

- SQLite/PostgreSQL

- Pandas, scikit‑learn (likely, inside builder)

- Django templating + JS + CSS for frontend

- themoviedb.org REST API

5. Guidelines for tailoring the diagram

- Full‑stack app emphasis: draw a clear separation between frontend (templates/static/js in templates/ & static/ folders) and backend (Django server with multiple apps).

- Show the offline pipeline separately: builder & evaluator modules as batch jobs.

- Highlight the database as the shared state across ingestion, modeling, evaluation, and serving.

- Emphasize external dependency on themoviedb.org for poster data.

6. Diagram elements to include

- Labels: “Browser”, “Docker Container: Django Web App”, “S3 / Local Disk (model artifacts)”, “Database (SQLite/Postgres)”, “External API: themoviedb.org”, “Batch Pipeline (builder, evaluator)”.

- Arrows:

• From Browser → Web App (HTTP GET/POST)

• Web App → Database (read/write)

• collector → Database (ingest)

• builder/evaluator → Database (read/write metrics/models)

• recommender app → model artifacts storage

• Web App → themoviedb.org (fetch posters)

- Color/shape coding:

• Rectangles for services/apps, cylinders for databases, clouds for external APIs, parallelograms for batch jobs.

7. Emphasis on detail

- Draw each Django app as a separate box inside the Web App boundary.

- Show the recs/ library as a shared component used by both builder and recommender apps.

- Illustrate the lifecycle: code → container build → DB migration → data population → model building → evaluation → serving.

- Ensure each arrow is labeled with the type of interaction (e.g., “HTTP request”, “SQL query”, “Model artifact read”).

- Use consistent colors or shapes to distinguish online (serving) vs. offline (batch) components.

By following these steps and including all listed elements, you’ll produce a clear, detailed, and accurate system design diagram tailored to the MovieGEEK project.

</explanation>