Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
-
Updated
Jun 7, 2024 - Java
Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
Machine Learning Platform and Recommendation Engine built on Kubernetes
个性化新闻推荐系统,A news recommendation system involving collaborative filtering,content-based recommendation and hot news recommendation, can be adapted easily to be put into use in other circumstances.
RiVal recommender system evaluation toolkit
Java-Based Context-aware Recommendation Library
Towards A Standardized Tag Recommender Benchmarking Framework
Recommender system based on Flink and Reinforcement Learning
🏷️ An e-commerce marketplace template. An online auction and shopping website for buying and selling a wide variety of goods and services worldwide.
An implementation of locality sensitive hashing with Hadoop
A news recommendation evaluation framework
基于 Spark Streaming 的电影推荐系统
A simple movie recommendation api using apache mahout machine learning library.
Recommendation engine in Java. Based on an ALS algorithm (Apache Spark). Train a new model after N seconds.
Software for the experiments reported in the RecSys 2019 paper "Multi-Armed Recommender System Bandit Ensembles"
A Java framework to build semantics-aware autoencoder neural network from a knowledge-graph.
基于 Spark 的微服务推荐系统
The book recommendation system is a web application developed using the Java Spring Boot framework and under it's the principles of software clean architecture. The system recommendations based on user-based collaborative filtering
This is a Java-based benchmark for matrix approximation based collaborative filtering
SpringBoot + Apache Mahout 推荐引擎 基于用户评分数据推荐相关电影
Completed exercises for Coursera Recommender Systems MOOC
Add a description, image, and links to the recommender-system topic page so that developers can more easily learn about it.
To associate your repository with the recommender-system topic, visit your repo's landing page and select "manage topics."