This content originally appeared on DEV Community and was authored by mahmoudabbasi
Many people think you need huge datasets like Netflix or Amazon to build a recommender system. The truth is: you can start small — with a simple model — and let it improve itself over time as users interact with your product.
In this post, we'll build a self-learning recommender system that:
- Starts with a pre-trained model (solves the cold-start problem)
- Learns from user interactions over time (incremental learning)
Updates recommendations to stay relevant
The Problem
When you first launch a product, you usually have no idea what each user likes. If your recommender starts giving random suggestions, users might leave before the system learns anything.
To avoid this, we start with a pre-trained model based on your historical sales or click data. This allows your system to give at least reasonable recommendations on day one.
- Building the Initial Model
Let's assume you have a small e-commerce store selling shoes, t-shirts, and accessories. You have some historical purchase data like this:
import pandas as pd
# Historical data
data = pd.DataFrame({
"userId": [1, 1, 2, 2, 3],
"productId": ["T-Shirt", "Shoes", "Shoes", "Jacket", "Hat"],
"category": ["Clothes", "Shoes", "Shoes", "Clothes", "Accessories"]
})
# Step 1: Find most popular categories
category_popularity = data.groupby("category")["productId"].count().sort_values(ascending=False)
def initial_recommendations(user_id):
return list(category_popularity.index) # Recommend popular categories first
print(initial_recommendations(1))
# Output: ['Shoes', 'Clothes', 'Accessories']
With just a few lines of code, you have a model that suggests the most popular categories for new users.
- Making It Self-Learning
Now let's say user 1 buys another pair of shoes. Our system should learn from this and boost the weight for "Shoes" in that user's profile.
# A simple way to store user preferences
user_profiles = {
1: {"Shoes": 1, "Clothes": 1},
2: {"Shoes": 2, "Clothes": 1}
}
# Update function when a new purchase happens
def update_profile(user_id, category):
user_profiles.setdefault(user_id, {})
user_profiles[user_id][category] = user_profiles[user_id].get(category, 0) + 1
# User 1 buys new shoes
update_profile(1, "Shoes")
print(user_profiles[1]) # {'Shoes': 2, 'Clothes': 1}
Now we can recommend categories based on this updated profile — focusing on what each user really likes.
- The Learning Cycle
- Start with pre-trained model (based on historical data)
- Serve recommendations to the user
- Track interactions (click, purchase, like/dislike)
- Update user profile or model weights
- Generate improved recommendations
This cycle continues, and your system becomes smarter with every interaction.
- Advantages of This Approach
✅ Better cold-start performance – users get meaningful suggestions from day one
✅ Personalization over time – recommendations adapt to user behavior
✅ Scalable – works with small data, can grow into ML models like ALS, bandits, or deep learning later
- Next Steps
If you want to take this further, you could:
- Use ALS (Alternating Least Squares) in Spark for collaborative filtering
- Implement multi-armed bandits for real-time optimization
- Combine content-based + collaborative filtering for hybrid recommenders
Would you like me to write a follow-up on building this with Spark or deep learning? Leave a comment below!
This approach lets you build a recommender that doesn't stay static — it learns and adapts with your users.
This content originally appeared on DEV Community and was authored by mahmoudabbasi
mahmoudabbasi | Sciencx (2025-09-24T10:14:10+00:00) Building a Self-Learning Recommender System (Without Needing Netflix Data!). Retrieved from https://www.scien.cx/2025/09/24/building-a-self-learning-recommender-system-without-needing-netflix-data-2/
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