The Book Recommendation System provides personalized book suggestions using Popularity-Based Recommender, Collaborative Filtering, and Cosine Similarity. Implemented with Flask, it allows users to enter a book title and receive tailored recommendations based on their preferences.

Book Recommendation System - Unsupervised

This project explores diverse "Recommendation Techniques", each offering a distinct approach to predicting user preferences.

Book recommendation system using Popularity based and Collaborative filtering approaches.

This is my Sem 7 ML Lab Project. For complete details, kindly check the below README File.

The Book Recommendation System is designed to assist users in discovering books that align with their personal interests and reading habits. This system aims to address the challenge of information overload. The combination of a robust recommendation engine and an intuitive user interface ensures that users have a seamless experience.

A book recommendation system that uses Natural Language Processing to analyze book descriptions, reviews, or metadata. Built with Python and NLP libraries like spaCy, NLTK, and scikit-learn to provide content-based recommendations.

Book Recommendation System

This is my Sem 7 NLP Lab Project . For complete details, kindly check below README File.

Hybrid Movie Recommendation System offering personalized and popularity-based suggestions using content-based and collaborative filtering, with FastAPI backend, Streamlit frontend, and Dockerized deployment for interactive movie discovery.

This project aims to develop a robust book recommendation system using Python and Flask . Leveraging extensive datasets of book information and user interactions, the system employs advanced machine learning algorithms like collaborative filtering to provide personalized recommendations.

a streamlit-based book recommendation system using machine learning

A smart Book Recommendation System that suggests trending books using popularity and personalized picks with collaborative filtering.

App that recommends insurance products based on popularity, content and user similarity.

Research and Development of a Product Recommendation System using Deep Matrix Factorization

Movie Recommendation System using Popularity, Content and Collaborative Based Recommendation methods

This project is a Book Recommendation System that uses two main approaches: Popularity-Based and Collaborative Filtering. It recommends top books based on their rating frequency and average ratings, and also provides personalized book suggestions by analyzing user interactions.

Suggestify-RecommendationSystems is a dedicated repository for implementing and experimenting with traditional recommendation system techniques. It covers collaborative filtering, content-based methods, and hybrid approaches, focusing on practical and scalable solutions for personalised recommendations across various domains.

Popularity-based recommendations are a simple baseline in recommender systems that recommend the most popular items to all users. These systems are widely used as a reference model in research and industry because of their simplicity and ability to function without user history.