April 16, 2025

Best Recommendation System project ideas for beginners [With Source Code]

Are you ready to explore recommendation systems as a beginner? Learning through hands-on projects is the best way to understand core concepts and build real-world skills.

Here is a list of beginner-friendly recommendation system projects that will strengthen your knowledge and help you grow in the AI field.

10 Beginner-Friendly Recommendation System Project Ideas – Overview

Here’s an overview of the 10 best Recommendation System Project Ideas for beginners:

S.No.	Project Title	Complexity	Estimated Time	Source Code
1	Movie Recommendation System using Cosine Similarity	Easy	5 hours	Get Started
2	Book Recommendation using Collaborative Filtering	Easy	6 hours	Get Started
3	Music Recommendation using Spotify API	Easy	7 hours	Get Started
4	YouTube Video Recommender (Keyword-Based)	Easy	8 hours	Get Started
5	Product Recommender using Item-Based Filtering	Easy	9 hours	Get Started
6	News Article Recommendation using NLP & TF-IDF	Medium	10 hours	Get Started
7	Movie Recommendation with SVD (Matrix Factorization)	Medium	11 hours	Get Started
8	Fashion Outfit Recommender Using Image Similarity	Medium	13 hours	Get Started
9	Personalized E-commerce Recommender (Hybrid Model)	Hard	15 hours	Get Started
10	Deep Learning-based Netflix Clone Recommender	Hard	18 hours	Get Started

Top 10 Recommendation System project ideas for beginners

Here are the top 10 simple recommendation system project ideas for beginners:

1. Movie Recommendation System using Cosine Similarity

This project recommends movies based on metadata such as genre, cast, and keywords using cosine similarity.

You will learn how to implement content-based filtering using text vectorization techniques.

Duration: 5hrs

Project Complexity: Easy

Key Concepts Covered:

Cosine similarity
Content-based filtering
TF-IDF vectorization

Implementation Steps:

Load movie dataset
Extract features (genre, cast, keywords)
Convert text to vectors
Compute similarity matrix
Recommend top similar movies

Required Pre-requisites:

Python basics
Pandas & NumPy
scikit-learn

Resources Required:

TMDb or IMDb dataset
Jupyter Notebook
scikit-learn

Real-World Application:

Movie suggestion in streaming platforms
Genre-based content filtering

Get Started

2. Book Recommendation using Collaborative Filtering

This project suggests books based on ratings and preferences of similar users using collaborative filtering.

You will learn how to build user-based recommender systems using similarity scores.

Duration: 6hrs

Project Complexity: Easy

Key Concepts Covered:

User-item matrix
Collaborative filtering
Similarity computation

Implementation Steps:

Import ratings dataset

Create user-item matrix
Calculate user similarity
Recommend books based on neighbors
Evaluate model output

Required Pre-requisites:

Python basics
Matrix operations
Pandas

Resources Required:

Book-Crossing dataset
Jupyter Notebook
scikit-learn

Real-World Application:

Bookstore recommendation systems
Personalized reading suggestions

Get Started

3. Music Recommendation using Spotify API

This project recommends music by analyzing audio features from user playlists using the Spotify API.

You will learn how to work with external APIs and filter tracks based on feature similarity.

Duration: 7hrs

Project Complexity: Easy

Key Concepts Covered:

API integration
Audio feature analysis
Track similarity

Implementation Steps:

Connect to Spotify API
Fetch playlist data
Analyze track features
Compare songs using similarity
Recommend new tracks

Required Pre-requisites:

API handling in Python
JSON parsing
Pandas

Resources Required:

Spotify Developer Account
Spotipy
Jupyter Notebook

Real-World Application:

Music discovery tools
Personalized playlist generators

Get Started

4. YouTube Video Recommender (Keyword-Based)

This project recommends videos by comparing metadata like titles and tags using text similarity.

You will learn basic NLP techniques such as keyword extraction and TF-IDF.

Duration: 8hrs

Project Complexity: Easy

Key Concepts Covered:

Keyword extraction
TF-IDF vectorization
Text similarity

Implementation Steps:

Load video metadata
Preprocess text
Convert to TF-IDF vectors
Compute similarity scores
Recommend related videos

Required Pre-requisites:

Python basics
NLP fundamentals
Text processing

Resources Required:

YouTube metadata dataset
scikit-learn
Jupyter Notebook

Real-World Application:

Smart content recommendations
Video suggestion engines

Get Started

5. Product Recommender using Item-Based Filtering

This project recommends products based on purchase history using item-item similarity.

You will learn to implement collaborative filtering by analyzing item co-occurrence.

Duration: 9hrs

Project Complexity: Easy

Key Concepts Covered:

Item similarity
Transaction matrix
Recommendation logic

Implementation Steps:

Import transactional data
Create item-item matrix
Compute similarity
Identify top related items
Recommend products

Required Pre-requisites:

Python basics
Matrix manipulation
scikit-learn

Resources Required:

Retail transaction dataset
Pandas
Jupyter Notebook

Real-World Application:

E-commerce product suggestions
Upselling and cross-selling tools

Get Started

6. News Article Recommendation using NLP & TF-IDF

This project recommends similar news articles by analyzing content with TF-IDF and cosine similarity.

You will learn how to apply NLP preprocessing and build content-based recommenders.

Duration: 10hrs

Project Complexity: Medium

Key Concepts Covered:

Text preprocessing
TF-IDF
Cosine similarity

Implementation Steps:

Clean and tokenize articles
Create TF-IDF matrix
Compute similarity between articles
Build recommendation function
Display results

Required Pre-requisites:

Python basics
NLP libraries
scikit-learn

Resources Required:

BBC or Kaggle News dataset
NLTK or spaCy
Jupyter Notebook

Real-World Application:

News personalization engines
Related content suggestions

Get Started

7. Movie Recommendation with SVD (Matrix Factorization)

This project uses Singular Value Decomposition to predict movie ratings and recommend unseen titles.

You will learn how to use matrix factorization for collaborative filtering.

Duration: 11hrs

Project Complexity: Medium

Key Concepts Covered:

SVD
Latent factor modeling
Rating prediction

Implementation Steps:

Create user-movie matrix
Apply SVD
Reconstruct matrix
Predict missing values
Recommend top-rated movies

Required Pre-requisites:

Matrix algebra
NumPy
Recommender basics

Resources Required:

MovieLens dataset
scikit-surprise
Matplotlib

Real-World Application:

Personalized movie prediction
Rating-based filtering

Get Started

8. Fashion Outfit Recommender Using Image Similarity

This project recommends similar outfits by comparing visual features of clothing images.

You will learn to extract image embeddings using pre-trained CNNs.

Duration: 13hrs

Project Complexity: Medium

Key Concepts Covered:

Image embeddings
CNNs
Similarity metrics

Implementation Steps:

Load fashion images
Extract features using CNN
Compute image similarity
Recommend similar outfits
Visualize results

Required Pre-requisites:

CNN basics
OpenCV or PIL
TensorFlow/Keras

Resources Required:

DeepFashion dataset
Pre-trained CNN models
Jupyter Notebook

Real-World Application:

Visual shopping experience
Style matching platforms

Get Started

9. Personalized E-commerce Recommender (Hybrid Model)

This project builds a hybrid recommendation system combining collaborative and content-based methods.

You will learn to merge different scoring strategies for improved accuracy.

Duration: 15hrs

Project Complexity: Hard

Key Concepts Covered:

Hybrid recommendation
Feature engineering
Scoring fusion

Implementation Steps:

Load user-item and metadata
Build collaborative and content models
Merge outputs using hybrid score
Recommend products
Tune model parameters

Required Pre-requisites:

Python ML libraries
Collaborative filtering knowledge
Feature selection

Resources Required:

Retail dataset
Pandas
scikit-learn

Real-World Application:

Personalized product recommendations
Intelligent e-commerce platforms

Get Started

10. Deep Learning-based Netflix Clone Recommender

This project builds a neural recommender using deep learning to model user-item interactions.

You will learn how to apply neural collaborative filtering with embeddings.

Duration: 18hrs

Project Complexity: Hard

Key Concepts Covered:

Deep learning
Embedding layers
Neural CF

Implementation Steps:

Prepare user-item pairs
Build embedding-based model
Train neural network
Predict user preferences
Recommend unseen content

Required Pre-requisites:

Deep learning basics
TensorFlow or PyTorch
Recommender system knowledge

Resources Required:

MovieLens dataset
TensorFlow/Keras
GPU (optional)

Real-World Application:

AI-based content recommendation
Viewer personalization engines

Get Started

Frequently Asked Questions

1. What are some easy recommendation system project ideas for beginners?

Some easy recommendation system project ideas for beginners include movie recommenders using cosine similarity, book recommenders with collaborative filtering, and keyword-based YouTube video recommenders.

2. Why are recommendation system project ideas important for beginners?

Recommendation system project ideas are important for beginners because they show how machine learning can be applied to real-world personalization problems.

3. What skills can beginners learn from the recommendation system Project?

From the recommendation system project, beginners can learn similarity measures, matrix operations, data preprocessing, and filtering techniques like content-based and collaborative methods.

4. Which recommendation system project is recommended for someone without programming experience?

A content-based movie recommendation system using cosine similarity is recommended for someone without programming experience as it is simple and easy to implement.

5. How long does it typically take to complete a beginner-level recommendation system project?

A beginner-level recommendation system project typically takes around 6 to 8 hours, depending on the project scope and Python knowledge.

Final Words

Recommendation system projects are a great way to start learning how data and machine learning work together. They help you build practical skills by working on real examples.

Start with the easy ones, try different ideas, and slowly move to advanced projects. Keep building—every project you complete brings you closer to becoming job-ready in the AI field!

Explore More Project Ideas

Thirumoorthy

Thirumoorthy serves as a teacher and coach. He obtained a 99 percentile on the CAT. He cleared numerous IT jobs and public sector job interviews, but he still decided to pursue a career in education. He desires to elevate the underprivileged sections of society through education