🍃 Recommender System in JavaScript for the MovieLens Database

Local recommendation system

A github repository suggestion system

Movies Reviewed by people, for people

基于深度学习的商品推荐系统，高性能，可承受高并发，可跨平台

Chrome extension for creating custom Smart Replies in Gmail

Yum-me is a nutrient based food recommendation system

The Web IR / NLP Group (WING)'s public reading group at the National University of Singapore.

This is a book recommendation app created with React 18.2 and MUI for coders/programmers looking for reccomendations to books on programming/coding to read

The main aim of the project is to develop a web-based application that is going to make it possible for the customer to place an order of food by using this app . In this we are also creating food recommendation app and that will substitute the manual system of the placing an order with an automated one.

The Internet plays an increasingly important part in our daily lives as a source of written content for news and leisure. Yet it is tedious and difficult to sort through this staggering flow of information and stay updated with changes in our world, even using automated tools. Reading magazines and newspapers is too time-consuming, and there is …

Keep-Watching is a movie recommendation and review app build using React for the front-end, express for the back-end, flask for handling the recommendations and similar movies section, mongoDB for the database, passport.js for handling social media logins

Recommender system engine on NodeJS

🌠 Recognition platform built for developer endorsements

Flask based Movie Recommendation System

A fully functional Spotify clone! 🎧

Content based recommendation using elasticsearch

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Legal Up recommends suitable lawyers⚖️ to clients based on concise case descriptions🔍 using advanced algorithms, ensuring clients find the right legal expertise. 💼

An indoor plant recommendation system built using React.js and Node.js. The system leverages expert systems and multi-agent systems to provide personalized recommendations for indoor plants based on user preferences and environmental factors.