What Even Is a Recommendation System?

You know when Netflix suggests a show and you think, "How did it know?" Or when Amazon shows you products that are weirdly perfect for you? That's a recommendation system at work. It's basically software that looks at what you (and people like you) have done in the past, and uses that to guess what you'll probably want next.

The good news? You don't need to be Netflix to build one. If you're a solo developer building an app, a blog, or an online store, you can add recommendations to keep your users coming back. Let's break down how these things actually work, in plain English, with real examples you can follow.

The Two Main Approaches (With Real Examples)

There are really only two core ideas behind every recommendation system. Once you understand these, everything else is just variations on the theme.

1. Collaborative Filtering: "People Like You Also Liked..."

This approach looks at what similar users have done. It doesn't care what the content actually is. It just notices patterns in behavior.

Real-world example: Say you're building a book review app. User A loved books #1, #2, and #3. User B loved books #1 and #2 but hasn't seen book #3 yet. Collaborative filtering says, "User B will probably love book #3 too, because they have the same taste as User A."

This is exactly how Spotify's "Discover Weekly" playlist works. It finds users with similar listening habits to yours, then recommends songs they loved that you haven't heard yet.

2. Content-Based Filtering: "Because You Liked This Specific Thing..."

This approach looks at the actual characteristics of what a user has interacted with, then finds more stuff with similar traits.

Real-world example: You're building a recipe app. A user keeps saving vegetarian pasta recipes that take under 30 minutes. Content-based filtering would recommend other quick vegetarian pasta dishes, even if no other user has tried them yet, because the ingredients and attributes match what this user clearly prefers.

YouTube's "Related Videos" sidebar works this way. It looks at the tags, title, and category of what you're watching, then finds videos with similar attributes.

Two Types of Recommendation Systems

👥

Collaborative Filtering
"Users like you also enjoyed..." (Spotify, Amazon)

🔍

Content-Based Filtering
"Because this item has similar traits..." (YouTube, Netflix)

🧪

Hybrid (Best of Both)
Combine both for better results (what most big apps do)

📈

Popularity-Based (Easiest Start)
"Most popular this week" - no user data needed

⏱️

Real-Time Updates
Adapt instantly as users interact with your app

What About Embeddings? (It's Simpler Than It Sounds)

You'll hear the term "embeddings" a lot in recommendation system tutorials. It sounds intimidating, but the idea is dead simple.

Think of it like this: imagine every user and every item in your app gets placed on a giant map. Users who like similar things end up close together on the map. Items that are similar also end up close together. An "embedding" is just the coordinates (the location) of a user or item on that map.

Real-world example: In a music app, a user who listens to jazz, blues, and soul would be placed near those genres on the map. A user who listens to metal and punk would be in a completely different area. When a new jazz album comes out, the system checks which users are "close" to jazz on the map and recommends it to them.

Why This Matters If You're Building an App Alone

If you're a solo developer, adding even a simple recommendation system can be a game-changer for keeping users engaged. Here's why:

Users stick around longer. When people see content that feels personally picked for them, they spend more time in your app. Think about how you keep scrolling TikTok because it keeps showing you exactly what you're into.
Users discover more of your content. If you have a blog with 200 posts, most visitors will only see the latest few. A "You might also like" section can surface older posts that are still relevant to each reader.
Users come back more often. Personalized email digests ("Here's what's new based on your interests") give people a reason to return. This is how Medium keeps you clicking on articles.

How to Actually Build One (Step by Step)

Here's a practical roadmap that won't overwhelm you:

Start by tracking what users do. Before you can recommend anything, you need data. Track clicks, likes, saves, time spent on pages, purchases, whatever makes sense for your app. Even a simple database table like user_id | item_id | action | timestamp is enough to start.
Begin with the simplest approach. Don't jump straight to machine learning. Start with "most popular items" or "users who bought X also bought Y." For a small app, a few SQL queries can power surprisingly good recommendations.
Graduate to a library when you're ready. Python's Surprise library makes collaborative filtering dead simple. You can have a working recommendation model in about 20 lines of code. TensorFlow Recommenders is great if you want to go deeper.
Keep it fresh. Recommendations get stale fast. Set up a weekly job to retrain your model with new data. A user's taste in January might be completely different by March.

If you don't want to build it yourself, managed services can handle the heavy lifting. AWS Personalize lets you feed in your data and get recommendations back through an API. Google's Recommendations AI does the same thing. Both have free tiers that are generous enough for small apps.

Building Your First Recommendation System

Track User Actions

Log clicks, likes, purchases, and time spent

Start Simple

"Most popular" or "also bought" with basic SQL queries

Add a Library

Use Python's Surprise or TensorFlow Recommenders

Retrain Weekly

Keep recommendations fresh as user tastes change

Measure What Works

Track click-through rates on your recommendations

Key Takeaways

You don't need to be a data scientist. A simple "people who liked X also liked Y" query can work wonders for a small app.
Start with what you have. Even basic click data is enough to power useful recommendations.
There are free tools that do the heavy lifting. Libraries like Surprise and managed services like AWS Personalize have free tiers perfect for solo developers.
Keep it fresh. Retrain your model regularly. Stale recommendations are worse than no recommendations.
Measure the impact. Track whether users actually click on your recommendations. If they don't, tweak your approach.

Conclusion

Recommendation systems aren't just for tech giants with massive engineering teams. As a solo developer, you can start with something as simple as "most popular this week" and gradually work your way up to personalized suggestions powered by real user data. The tools are free, the libraries are beginner-friendly, and the impact on user engagement can be huge.

The key is to just start. Track user behavior, try a simple approach, and iterate from there. Your users will notice the difference.

For further reading, check out Google's Recommendation Systems Guide, AWS Personalize, and the Surprise library documentation.