A recommender engine similar to those used by Netflix or Amazon.

[GIW-MII-UGR-2016-2017] Desarrollo de un Sistema de Recomendación basado en Filtrado Colaborativo "User Based" desde cero y con Mahout Taste

This project is proof-of-concept work of developing Mahout Recommendation Engine. The technology used is Spring Boot framework and MongoDB as the database.

Java and Spring demo API for the Oatfin platform.

some examples via spark,hadoop

recommend

A sample of my work from APCS (AP Computer Science) at Homestead High School.

ML Recommendation System Skeleton / Product Comparison Service (Java 11 / MongoDB / Spring)

A media recommendation web app.

Maidenpool SDK for the Java programming language.

A DIY version of a simple movie recommendation system.

In this project we build docker deployable adaptive recommendation engine for meals to maintain users’ nutritional intake and variety in upcoming meals using Flask. We encode preparation steps of recipes in vector space to find similarities between recipes using math formula. We develop interactive Android App for users to log daily meals.

a Java-based recommendation engine using t-SNE techinal and QuadTree algorithms

Java and Spring demo api for the Oatfin platform

Apache Spark Recommendation/Machine Learning Api Service

Generic Recommendation Engine Skeleton

A Java implementation of Alternating Least Squares (ALS).

Spring Boot Starter for using Mahout as a recommendation engine for item-based collaborative filtering.

A recommendation system, complete with UI built using Java in IntelliJ IDEA

Book Recommendation Service