How to Build a Recommendation System from Scratch

A recommendation system helps suggest items (like movies, products, or music) to users based on their preferences or behavior.

Step 1: Understand the Types of Recommendation Systems

1. Collaborative Filtering

Recommends items based on user behavior (e.g., users who liked this also liked that).

Two types:

User-based: Find users similar to you.

Item-based: Find items similar to what you like.

2. Content-Based Filtering

Recommends items similar to what the user liked before, based on item features (e.g., genres, descriptions).

3. Hybrid Systems

Combine both collaborative and content-based methods.

Step 2: Collect and Prepare Data

You need data about users, items, and their interactions.

Example data:

User IDs

Item IDs

Ratings or clicks (explicit or implicit feedback)

Item features (genre, category, etc.)

Clean and preprocess data:

Handle missing values

Normalize ratings

Encode categorical features

Step 3: Choose a Recommendation Algorithm

Collaborative Filtering (User-based)

Compute similarity between users (e.g., cosine similarity, Pearson correlation)

Predict ratings based on neighbors' ratings

Collaborative Filtering (Item-based)

Compute similarity between items

Recommend items similar to those a user liked

Content-Based Filtering

Use features of items (e.g., TF-IDF for text, tags)

Compute similarity between items and user profile

Matrix Factorization (Advanced)

Techniques like SVD to decompose the user-item rating matrix

Predict missing ratings efficiently

Step 4: Build the Model

Calculate similarity scores (user-user or item-item)

Predict ratings or rankings

Generate a list of top-N recommendations

Step 5: Evaluate the System

Use metrics like:

Precision and Recall

Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) (for rating prediction)

F1 Score

Mean Average Precision (MAP)

Step 6: Deploy and Improve

Build an interface (website/app) to display recommendations

Continuously collect user feedback to improve the model

Experiment with hybrid approaches and deep learning models for better accuracy

Bonus: Simple Example Using Python (Collaborative Filtering)

import numpy as np

from sklearn.metrics.pairwise import cosine_similarity

# Sample user-item rating matrix

ratings = np.array([

[5, 3, 0, 1],

[4, 0, 0, 1],

[1, 1, 0, 5],

[0, 0, 5, 4],

[0, 1, 5, 4],

])

# Compute item-item similarity

item_similarity = cosine_similarity(ratings.T)

print("Item similarity matrix:\n", item_similarity)

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October 03, 2025

Friday, October 3, 2025

How to Build a Recommendation System from Scratch