StreamCompass

🧭 StreamCompass

Navigate the streaming landscape - find your next watch and where to watch it.

StreamCompass is a content-based recommendation engine that takes a show or movie you love and surfaces similar titles across streaming platforms. Tell us what you just watched, and we’ll point you to what’s next.

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Features

• Title Search - search any movie or show to use as your starting point
• Smart Recommendations - similarity scoring based on genre, cast, tags, and more
• Platform Mapping - see which streaming services carry each recommended title
• Filter & Explore - filter results by platform or genre
• Clean UI - dark-mode interface built for the streaming experience

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Tech Stack

Frontend

• React + Vite
• Tailwind CSS

Backend

• FastAPI (Python)
• scikit-learn (TF-IDF + cosine similarity)
• pandas / numpy

Data

• MovieLens Dataset
• Streaming platform availability data via Kaggle

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Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --reload

The API will be running at http://localhost:8000. Docs available at http://localhost:8000/docs.

Frontend Setup

cd frontend
npm install
npm run dev

The app will be running at http://localhost:5173.

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API Endpoints

Method	Endpoint	Description
GET	/search?q={title}	Search for a title
POST	/recommend	Get recommendations for a given title
GET	/platforms	List all supported streaming platforms

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How It Works

1. User searches for a title
2. Backend looks up the title in our dataset and extracts its feature vector (genre, cast, tags, etc.)
3. TF-IDF vectorization + cosine similarity is computed across all titles in the dataset
4. Top N most similar titles are returned alongside their streaming platform availability
5. Results are ranked by similarity score and displayed in the UI

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Project Structure

StreamCompass/
├── backend/
│   ├── main.py            # FastAPI app and routes
│   ├── recommender.py     # Recommendation engine logic
│   ├── data_loader.py     # Dataset loading and preprocessing
│   ├── requirements.txt
│   └── data/              # Dataset files
├── frontend/
│   ├── src/
│   │   ├── components/    # React components
│   │   ├── pages/         # Page views
│   │   └── api/           # API call helpers
│   ├── index.html
│   └── package.json
└── README.md

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Team

Name	Role
Ashsmith Khayrul	ML & Backend
Angie Che	Frontend & Integration

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Course

CS 4100 - Artificial Intelligence Northeastern University, Summer 2026

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