Project Overview: Knowledge-based, Content-based and Collaborative Recommender systems are built on MovieLens dataset with 100,000 movie ratings.

You just finished a book! Now what? Using KNN and NLP, I can give you the next five books on your reading list based on the one you just finished. 

A book recommender system is a type of a type of recommendation system where we have to recommend similar books to the reader based on his interest. In this project, we will implement the populartity based recommender system and collabrative based recommender system to build a book recommender system.

Uses machine learning algorithms to suggest products to users based on their past interactions and preferences. It involves collecting user data, analyzing it, and using algorithms such as collaborative filtering or content-based filtering to generate personalized recommendations.

Item-based collaborative filtering makes recommendations based on user-product interactions in the past.

This repository is based on the lecture '고객데이터와 딥러닝을 활용한 추천시스템 구현'

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

The application uses content based filtering to make recommendations. For every movie selected, 12 recommendations are made based on their cosine similarity with the selected movie. An API feteches the poster image of the movie and displays them in an image grid to the user The database offers nearly 5000 movies to select from

Content-based Movie Recommender System

Neural matrix factorization movie recommender paired with image similarity in poster design

The project used Python to create a personalized book recommendation system that analyzed users' past ratings on books to identify their preferences and patterns and suggested books that the user is likely to enjoy but has not read yet.

A recommendation engine for an e-commerce website using collaborative filtering

Recommendation System with Python

Build a recommender system by using cosine simillarties score - books dataset.

Movie Recommendation - Content Based

Team: SolveAI -> Solo Project - SheBuilds Hackathon.

Sentiment Based Item Recommender web app built using Flask.

ExcelR_Assignment---Recommendation-System---Assignment---10

Movie ratings through twitter tweets collected as dataset for Movies recommendation