Intelligent job recommendation platform using Java + MySQL + Redis. Supports location-based search, AI keyword extraction, and personalized suggestions.

RiVal recommender system evaluation toolkit

🏷️ An e-commerce marketplace template. An online auction and shopping website for buying and selling a wide variety of goods and services worldwide.

Personalized real-time movie recommendation system

基于 Spark Streaming 的电影推荐系统

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

A simple movie recommendation api using apache mahout machine learning library.

Mining and Utilizing Dataset Relevancy from Oceanographic Datasets to Improve Data Discovery and Access, online demo: https://mudrod.jpl.nasa.gov/#/

This is a personalization-based event recommendation systems for event search.

基于 Spark 的微服务推荐系统

A JavaFX music recommendation app that uses the Spotify API to create playlists.

🏠 DBsSA: Digital British-style student apartments, Stand-alone Deploy Version. (英式学生公寓数字化服务，单机部署版）

A Hybrid Recommendation model based on sentiment analysis on tweets and item based filtering to closely match preferred recommendation.

This project is an Android mobile application, written in Java programming language and implements a Recommender System using the k-Nearest Neighbors Algorithm. In this way the algorithm predicts the possible ratings of the users according to scores that have already been submitted to the system.

Intellij plugin development to source-code recommendation

Movie / Film recommendation system built using Java utilizing knowledge graph technology.

Book Recommendation Service

mobile application for managing car services, including data sync with local server and services recommendation using ML algorithms

A Java console application that implements the factionality of the knn algorithm to find the similarity between a new user with only a few non zero ratings of some locations, find the k nearest neighbors through similarity score and then predict the ratings of the new user for the non rated locations.