Book Recommendation System built for Book Lovers📖. Simply Rate ⭐ some books and get immediate recommendations🤩

A compiler for the 🎃 Spooky 😱 programming language 👻

A collection of papers on divergence and quality diversity

推荐算法个人学习笔记以及代码实战

This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀

Get an overview of sneak previews in your local cinema(s)

🎂 A beautiful, interactive mini website to wish my girlfriend a Happy Birthday! Built with ❤️ using HTML, CSS & JavaScript — featuring animations, surprises, and heartfelt moments. Perfect for anyone who wants to make their loved one’s birthday extra special online! 🌸✨

📱 Trigger easter eggs on mobile device

Client side escape room with mini-tasks leading to a final prize for @muskanrastogi1's birthday.

Project with examples of different recommender systems created with the Surprise framework. Different algorithms (with a collaborative filtering approach) are explored, such as KNN or SVD.

My lib.

Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows

A python notebook for building collaborative, content-based, and ml-based recommender systems with Sklearn and Surprise

Repository of OpenClassrooms' AI Engineer path, project #9 : create a books recommandation system, integrate and deploy it as a mobile app

surprise ur crush, friends, family, or lovely person to yourself with this flower 🌸🌹

Where do people look on images in average? At rare, thus surprising things! Let's compute them automatically

Farfetch: Understanding the customer

Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering

A recommender system that provides personalized movies recommendations to users based on their preferences and behavior using Collaborative Filtering.