How to build a recommendation system from scratch

build recommendation system from scratch
April 11, 2025
3 min read
By Cojocaru David & ChatGPT

Table of Contents

This is a list of all the sections in this post. Click on any of them to jump to that section.

index

How to Build a Recommendation System from Scratch: A Step-by-Step Guide

Want to build a recommendation system like Netflix or Amazon? This step-by-step guide breaks down how to create one from scratch using collaborative filtering, content-based filtering, and hybrid models. You’ll learn data preparation, algorithm selection, evaluation, and deployment—everything you need to craft personalized recommendations.

Understanding Recommendation Systems

Recommendation systems predict user preferences to suggest relevant items. They power platforms like Spotify, YouTube, and e-commerce sites. The three main types are:

  • Collaborative Filtering: Recommends items based on user behavior (e.g., “Users who liked this also liked…”).
  • Content-Based Filtering: Suggests items similar to what a user has engaged with before (e.g., “If you liked sci-fi movies, try these”).
  • Hybrid Models: Combines both methods for better accuracy.

Each approach has trade-offs in scalability, accuracy, and data requirements.

Step 1: Data Collection and Preprocessing

A strong recommendation system starts with clean, structured data. Common datasets include:

  • User ratings (e.g., MovieLens dataset)
  • Purchase history (e.g., Amazon product buys)
  • Browsing behavior (e.g., clicks, time spent)

Key Preprocessing Steps

  • Handle missing data: Impute or remove incomplete entries.
  • Normalize ratings: Scale to a common range (e.g., 0–1) to avoid bias.
  • Encode categorical features: Use one-hot encoding for genres or categories.

Step 2: Choosing the Right Algorithm

Collaborative Filtering with Matrix Factorization

Matrix factorization uncovers hidden patterns in user-item interactions. Here’s a basic Python implementation using Singular Value Decomposition (SVD):

import numpy as np  
from scipy.sparse.linalg import svds  
 
R = np.array([[5, 3, 0, 1], [4, 0, 0, 1], [1, 1, 0, 5], [0, 0, 0, 4]])  
U, sigma, Vt = svds(R, k=2)  # k = latent factors  
predicted_ratings = np.dot(np.dot(U, np.diag(sigma)), Vt)

Explanation: SVD decomposes the user-item matrix to predict missing ratings. Adjust k to balance complexity and performance.

Content-Based Filtering with TF-IDF

For text-based recommendations (e.g., articles, products), use TF-IDF and cosine similarity:

from sklearn.feature_extraction.text import TfidfVectorizer  
from sklearn.metrics.pairwise import cosine_similarity  
 
documents = ["action movie", "comedy film", "sci-fi adventure"]  
vectorizer = TfidfVectorizer()  
tfidf_matrix = vectorizer.fit_transform(documents)  
similarity = cosine_similarity(tfidf_matrix[0], tfidf_matrix)

Explanation: This measures similarity between items based on their descriptions.

Step 3: Evaluating Your Model

Test performance with these metrics:

  • RMSE (Root Mean Squared Error): Measures rating prediction accuracy.
  • Precision@K: Checks if top-K recommendations are relevant.
  • A/B Testing: Compare real-world engagement (e.g., clicks, conversions).

Step 4: Deploying Your System

Choose a deployment method based on scalability needs:

  • Flask/Django: Lightweight APIs for small-scale systems.
  • TensorFlow Serving: For TensorFlow-based models.
  • Cloud Platforms (AWS SageMaker, Google AI): Scalable solutions.

“Great recommendation systems don’t just predict—they anticipate and delight.”

#recommendationsystems #machinelearning #datascience #AI #personalization