[GIW-MII-UGR-2016-2017] Desarrollo de un Sistema de Recomendación basado en Filtrado Colaborativo "User Based" desde cero y con Mahout Taste
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 recommender engine similar to those used by Netflix or Amazon.
some examples via spark,hadoop
Algorithm for recommend food based associations rule
User-Based Collaborative-Filtering recommendation engine built with Java
A media recommendation web app.
ML Recommendation System Skeleton / Product Comparison Service (Java 11 / MongoDB / Spring)
a Java-based recommendation engine using t-SNE techique and QuadTree algorithms
recommend
This project is proof-of-concept work of developing Mahout Recommendation Engine. The technology used is Spring Boot framework and MongoDB as the database.
A sample of my work from APCS (AP Computer Science) at Homestead High School.
Apache Spark Recommendation/Machine Learning Api Service
A recommendation system, complete with UI built using Java in IntelliJ IDEA
Maidenpool SDK for the Java programming language.
Spring Boot Starter for using Mahout as a recommendation engine for item-based collaborative filtering.
Java and Spring demo api for the Oatfin platform
Book Recommendation Service
Add a description, image, and links to the recommendation-engine topic page so that developers can more easily learn about it.
To associate your repository with the recommendation-engine topic, visit your repo's landing page and select "manage topics."