> For the complete documentation index, see [llms.txt](https://jrp.gitbook.io/notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jrp.gitbook.io/notes/ml-ai/video-recommendation/part-1-introduction.md).

# Part 1 - Introduction

Building a Video Recommendation System from Scratch: Part 1 - Introduction

Welcome to this beginner-friendly series on building a production-ready video recommendation system! If you've ever wondered how YouTube, Netflix, or TikTok suggest videos you might like, you're in the right place.

### What We'll Build

By the end of this series, you'll have built a complete recommendation system that:

* Analyzes user behavior (watches, likes, completions)
* Learns patterns from interaction data
* Predicts which videos a user might enjoy
* Serves recommendations through a REST API
* Can be deployed to the cloud (AWS SageMaker)

### Why Video Recommendations Matter

Think about your favorite streaming platform. Without recommendations, you'd have to browse through thousands of videos manually. A good recommendation system:

* Saves users time by surfacing relevant content
* Increases engagement by showing content users actually want
* Helps creators reach their target audience

### The Big Picture: How It Works

Our system follows a simple pipeline:

```
Raw User Data → Feature Engineering → Model Training → Predictions → API Server
```

Let me break this down in plain English:

1. **Raw User Data**: We collect how users interact with videos (did they watch it? like it? finish it?)
2. **Feature Engineering**: We convert this messy data into a clean format the computer can understand
3. **Model Training**: The computer learns patterns (e.g., "users who liked video A also liked video B")
4. **Predictions**: When a user visits, we predict which videos they'll probably like
5. **API Server**: We serve these recommendations through a web service

### What Makes This Different?

There are many recommendation approaches, but we're using **Collaborative Filtering** with **Matrix Factorization**. Here's the intuition:

Imagine you and your friend both love cooking videos and travel vlogs. If your friend also watches yoga videos, there's a good chance you'll like them too! The system finds these hidden connections automatically.

### The Technology Stack

Don't worry if these are unfamiliar - we'll explain everything:

* **Python**: Our programming language (easy to read and powerful for data)
* **Pandas**: Helps us organize data into tables
* **Implicit**: A library that implements the ALS algorithm (the "brain" of our system)
* **FastAPI**: Creates our web API (lets other apps request recommendations)
* **Docker**: Packages everything into a portable container

### Example Scenario

Let's look at a real example from our system:

**User u001** interacts with video v001:

* Watches the video (score: 1 point)
* Likes the video (score: 2 points)
* **Total rating: 3 points**

Later, when user u002 shows similar behavior patterns, we might recommend v001 to them!

### What You'll Learn

This series is divided into 5 parts:

1. **Part 1: Introduction** (you are here) - Understanding the big picture
2. **Part 2: Data Preparation** - Cleaning and organizing interaction data
3. **Part 3: The ALS Algorithm** - Training the recommendation model
4. **Part 4: Making Predictions** - Generating recommendations for users
5. **Part 5: Production Deployment** - Building an API and deploying to the cloud

### Prerequisites

To follow along, you should have:

* Basic Python knowledge (variables, functions, loops)
* Ability to run commands in a terminal
* Curiosity and patience!

You don't need to be an expert in machine learning or math. We'll explain concepts as we go.

### The Data We'll Use

Our dataset contains user interactions with videos:

```csv
userId,videoId,interactionType,interactionValue,location,timestamp
u001,v001,watch,1,USA,2025-01-01 10:10:00
u001,v001,like,2,USA,2025-01-01 10:20:00
u001,v002,watch,1,USA,2025-01-02 09:00:00
```

Each row represents one action:

* **userId**: Who performed the action
* **videoId**: Which video they interacted with
* **interactionType**: What they did (watch, like, complete)
* **interactionValue**: How much weight we give this action
* **location**: Where they were (we won't use this yet)
* **timestamp**: When it happened

### Key Concepts You'll Master

* **Collaborative Filtering**: Learning from similar users' preferences
* **Matrix Factorization**: Breaking complex data into simpler patterns
* **Sparse Matrices**: Efficiently storing mostly-empty data
* **ALS (Alternating Least Squares)**: The algorithm that powers our recommendations
* **REST APIs**: Serving predictions over the web

### What Success Looks Like

By the end, you'll run a command like:

```bash
curl -X POST http://localhost:8080/invocations \
  -H "Content-Type: application/json" \
  -d '{"user_id": "u001", "k": 3}'
```

And get back personalized video recommendations:

```json
{
  "userId": "u001",
  "recommendations": [
    {"videoId": "v005", "score": 0.91},
    {"videoId": "v004", "score": 0.045},
    {"videoId": "v003", "score": 0.0001}
  ]
}
```

### Ready to Start?

In the next part, we'll dive into data preparation - taking raw interaction data and transforming it into a format our model can learn from.

The code is simple, the concepts are powerful, and by the end, you'll have a working system you can deploy and customize!

**Source Code** : <https://github.com/jrphub/video-recommender>