Sci-Fi worth consuming

👍👎 A recommendation engine using Likes and Dislikes for your Ruby app

Ten thousand books, six million ratings

Recommendations for secure applications development with Rust

Java-Based Context-aware Recommendation Library

Resources recommended by members of our community. We welcome contributions, so feel free to send us pull requests adding things to the list! The resources in this list automatically get pushed to the http://progdisc.club/resources page.

Larafy is a Laravel package for Spotify API. It is more like a wrapper for the Spotify API.

Recommendation engines for developers, easy as π. https://tamber.com

Telemetry-Aware Addon Recommender

📚🐦 Patrick Collison on Twitter: Which five books have influenced you the most? (In terms of shaping your worldview.)

Market basket recommendations using association rules and apriori

BuiltOn Sneakers Demo Store

Recommendation engine in Java. Based on an ALS algorithm (Apache Spark). Train a new model after N seconds.

Dartmouth Course Reviews, Rankings, and Recommendations

Horror and thrillers worth consuming

This is an end to end Recommendations business scenario

🐙 Alternative course catalog for Karlsruhe Institute of Technology

A library class for WordPress plugins to tweak the results of the WordPress Plugins API with custom plugin recommendations.

Design and Implementation of a File Recommendation System Using Collaborative Filtering and Content-Based Recommendation for the Nextcloud Platform

Frequent Itemsets via Apriori Algorithm Apriori function to extract frequent itemsets for association rule mining We have a dataset of a mall with 7500 transactions of different customers buying different items from the store. We have to find correlations between the different items in the store. so that we can know if a customer is buying apple, banana and mango. what is the next item, The customer would be interested in buying from the store.

Constructor.io Client for Web Applications

Need help? Need ideas? Do you have ideas? Check our guides!

Recommendation for Amazon movie review data

This project is a set of scripts that allows to make an analysis of a database and to recommend music for users.

The JS Concord group is a group of developers striving for a more consistent JavaScript project development experience.

A GraphQL backend for MyAnimeRecs, which compute recommendations based on MyAnimeList and scraps many informations across many websites

GUI to help automate functions of the LibRec Java recommendation systems library

Recommends local movies to watch depending on imdb ratings