Step-by-Step: Build a Simple Recommendation System

 Step-by-Step: Build a Simple Recommendation System

Recommendation systems are the hidden engines behind Netflix’s movie suggestions, Amazon’s “People also bought” section, and Spotify’s personalized playlists.

At AI Computer Classes – Indore, you’ll learn how to build your own recommendation system — from basic logic to implementing it with Python and real-world datasets. This guide walks you through every step, making it ideal for beginners and professionals eager to explore AI-driven development.

🚀 What Is a Recommendation System?

A recommendation system filters and predicts what users might like based on patterns, preferences, and data.

There are mainly two types:

1. Content-Based Filtering – Recommends items similar to those a user liked before.
2. Collaborative Filtering – Recommends items liked by users with similar interests.

🧩 Example:

• Netflix suggests movies similar to what you’ve watched (content-based).
• Amazon shows products others with similar purchases also bought (collaborative).

🧩 Step 1: Prepare Your Dataset

You can start with a simple dataset — for example, movie ratings.

UserMovieRatingAInception5ATitanic4BInception4BAvatar5CTitanic3

💡 Use Excel or Power BI to quickly view and clean the dataset before feeding it into Python.

🧠 Step 2: Import Required Libraries

Use Python libraries to manage data and build your model.

import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

Load your dataset:

data = pd.read_csv("movies.csv")

🧮 Step 3: Create a User-Item Matrix

Transform the data into a pivot table:

user_movie_matrix = data.pivot_table(index='User', columns='Movie', values='Rating').fillna(0)

This matrix shows which user rated which movie — a foundation for similarity-based recommendations.

📊 Step 4: Compute Similarity Between Users

Use cosine similarity to identify users with similar tastes:

similarity = cosine_similarity(user_movie_matrix)
similarity_df = pd.DataFrame(similarity, index=user_movie_matrix.index, columns=user_movie_matrix.index)

💬 Example: If users A and B have similar preferences, we can recommend A the movies that B liked.

🔍 Step 5: Generate Recommendations

A simple recommendation function might look like this:

def recommend(user):
    similar_users = similarity_df[user].sort_values(ascending=False)[1:3].index
    recommended_movies = []
    for u in similar_users:
        top_movies = data[data['User'] == u].sort_values(by='Rating', ascending=False)['Movie'].head(2)
        recommended_movies.extend(top_movies)
    return set(recommended_movies)

Example Output:

print(recommend('A'))
# Output: {'Avatar'}

🧰 Step 6: Visualize Recommendations

Use Power BI or Matplotlib to visualize rating trends and top recommendations.

import matplotlib.pyplot as plt
top_movies = data.groupby('Movie')['Rating'].mean().sort_values(ascending=False)
top_movies.plot(kind='bar')
plt.title('Top Rated Movies')
plt.show()

💡 This helps identify the most popular items and refine your recommendation strategy.

🤖 Step 7: Enhance with ChatGPT or AI APIs

You can make your system smarter by integrating ChatGPT for content-based suggestions.

Example prompt:

“Suggest movies similar to Inception with a focus on sci-fi and thriller genres.”

ChatGPT can generate descriptive tags and summaries that you can include in your dataset for improved matching.

🧠 At AI Computer Classes – Indore, you’ll learn how to blend Python code with AI-powered logic for smarter systems.

🔧 Step 8: Deploy Your Model

Once you’re satisfied with your system:

• Build a simple Flask or Streamlit app for your recommendation interface.
• Connect your data dynamically with Excel or Google Sheets for easy updates.
• Host your app locally or on platforms like Render or Hugging Face Spaces.

This turns your local project into a working, shareable web app!

🧩 Tools You’ll Learn

ToolPurposeBenefitPython (Pandas, Scikit-learn)Core logicBuild algorithms easilyExcel / Power BIData preparationClean and analyze user dataChatGPTAI-driven enhancementAdd smart suggestionsFlask / StreamlitWeb deploymentBuild live apps fast

💡 Why Learn at AI Computer Classes – Indore?

We simplify complex programming concepts into hands-on, real-world projects.

You’ll build your own AI tools using:

• Python fundamentals
• Machine learning workflows
• Power BI dashboards
• API integrations

💬 Perfect for students and professionals aiming to enter the AI, analytics, or software development fields.

🏁 Conclusion

Recommendation systems are everywhere — from shopping sites to learning platforms.

Building one teaches you data handling, algorithm design, and AI fundamentals, all in one project.

Start your journey at AI Computer Classes – Indore, where we turn ideas into skills through practical, guided learning.

📞 Contact AI Computer Classes – Indore

✉ Email: hello@aicomputerclasses.com

📱 Phone: +91 91113 33255

📍 Address: 208, Captain CS Naidu Building, near Greater Kailash Road, opposite School of Excellence For Eye, Opposite Grotto Arcade, Old Palasia, Indore, Madhya Pradesh 452018

🌐 Website: www.aicomputerclasses.com