Source of truth : https://git.gregoiremureau.com/openclassroom-ai/oc-p10-content-recommendation

git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p10-content-recommendation.git
git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p10-content-recommendation.git

Dataset: see fetch-data.sh (excluded from git if large).

Content Recommendation System - My Content

📘 Ce projet est également disponible en français 🇫🇷

Project developing an article recommendation system based on a serverless Azure Functions architecture with a Streamlit user interface.

Project Deliverables

1. Working application: Streamlit + Azure Functions deployed
2. Versioned source code: Complete GitHub repository
3. Technical presentation: PDF deck (docs/presentation.pdf)

Overview

This project implements a content recommendation system using collaborative filtering and content-based algorithms. The serverless architecture enables automatic scaling and optimized cost.

Main Features

• Recommendation API: Azure Function returning the top 5 recommended articles for a user
• User interface: Streamlit application to test and demonstrate the system
• Implemented algorithms: Content-Based Filtering with precomputed embeddings
• Serverless architecture: Deployment on Azure Functions with Consumption plan

Analysis and Development

Jupyter Notebook

• Data exploration: Analysis of the Kaggle Globo.com dataset
• Algorithm implementation: Content-Based and Collaborative Filtering
• Approach comparison: Evaluation and technical choice
• PCA optimization: Dimensionality reduction 250D → 50D

Location: notebook/p10_notebook.ipynb

Architecture

Current MVP

[Streamlit Interface] → [Azure Functions API] → [Recommendation Algorithm]
                                ↓
                        [Embeddings data + user history]

Components

• Frontend: Streamlit (test interface)
• Backend: Azure Functions (Python 3.12, Linux)
• Algorithm: Content-Based Filtering with cosine similarity
• Data: Article embeddings (50D via PCA vs 250D original embeddings) + click history

Project Structure Overview

Project_10/
├── azure-app/                  # Azure Function
│   ├── function_app.py         # Azure Function entry point
│   ├── requirements.txt        # Python dependencies
│   ├── host.json              # Azure Functions configuration
│   ├── local.settings.json    # Local configuration (not versioned)
│   ├── data/
│   │   ├── embeddings_azure_50d.pkl    # Article embeddings (PCA 50D)
│   │   └── clicks_sample.csv           # User click history
│   └── utils/
│       ├── data_loader.py      # Data loading and caching
│       └── content_based.py    # Recommendation algorithm
├── notebook/
│   └── p10_notebook.ipynb     # Data analysis + model development
├── docs/
│   ├── presentation.pdf       # Presentation deck (15-25 slides)
│   └── illustrations/         # Architecture diagrams and screenshots
├── streamlit_app.py           # User interface
├── streamlit_requirements.txt # Streamlit dependencies
└── README.md

Installation and Deployment

Prerequisites

• Python 3.12+
• Azure CLI
• Azure Functions Core Tools
• Azure account

Azure Functions Deployment

1. Sign in to Azure

az login

2. Infrastructure creation

# Resource group
az group create --name rg-content-recommender --location francecentral

# Storage account
az storage account create --name [your-unique-storage-name] \
  --resource-group rg-content-recommender \
  --location francecentral \
  --sku Standard_LRS

# Linux Function App with Python 3.12
az functionapp create \
  --resource-group rg-content-recommender \
  --consumption-plan-location francecentral \
  --runtime python \
  --runtime-version 3.12 \
  --functions-version 4 \
  --name contentrecommender-linux \
  --storage-account stcontentrecommend2025 \
  --os-type Linux

3. Code deployment

# From the azure-app folder
func azure functionapp publish contentrecommender-linux

Streamlit Interface

1. Install dependencies

pip install -r streamlit_requirements.txt

2. Launch the interface

streamlit run streamlit_app.py

Usage

Azure Functions API

Endpoint: https://contentrecommender-linux.azurewebsites.net/api/recommend

Parameters:

• user_id (int): User identifier (0-706)

Example call:

curl "https://contentrecommender-linux.azurewebsites.net/api/recommend?user_id=0"

Response:

{
  "user_id": 0,
  "recommendations": [157519, 157944, 159495, 156690, 162857]
}

Streamlit Interface

1. Configure the API URL (local or Azure)
2. Select a user ID
3. Get the recommendations
4. Test the advanced features

Recommendation Algorithm

Content-Based Filtering

1. Data loading: Precomputed embeddings and user history
2. User profile computation: Average of the embeddings of the viewed articles
3. Cosine similarity: Computed against all articles in the catalog
4. Filtering: Exclusion of already-seen articles
5. Selection: Top 5 most similar articles

Optimizations

• Dimensionality reduction: PCA 250D → 50D to optimize Azure performance
• In-memory cache: Embeddings and user data loaded only once
• Serverless architecture: Automatic scaling based on demand

Tests and Validation

Local tests

# Start Azure Functions locally
func start

# Test the algorithm
python test_local.py

Performance metrics

• Response time: ~200ms per recommendation
• Supported users: 707 in the current dataset
• Available articles: 364,047 with embeddings

Known Issues and Solutions

Azure Deployment

• Python on Windows: Not supported, must use Linux
• Authentication: Set http_auth_level=func.AuthLevel.ANONYMOUS
• "Service unavailable" errors: Temporary Azure issues, retry later

Current Limitations

• Cold start: First request is slow after a period of inactivity
• New users: No handling of the cold-start problem
• Data updates: Manual process

Target Architecture (Future)

Planned Improvements

• Cold start handling: Popular recommendations for new users
• Data pipeline: Automatic embeddings update
• Database: Persistent storage of user profiles
• Hybrid API: Combination of Content-Based + Collaborative Filtering
• Distributed cache: Precomputation of popular recommendations

Technologies Used

• Backend: Azure Functions, Python 3.12
• Frontend: Streamlit
• ML/Data: scikit-learn, pandas, numpy
• Infrastructure: Azure CLI, Azure Storage
• Deployment: Azure Functions Core Tools

Author

Project carried out as part of an OpenClassrooms training program - AI Engineer.

License

This project is for educational purposes only.