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.
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Updated
May 21, 2019 - Java
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.
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
4 different recommendation engines for the MovieLens dataset.
This repository contains the code for building movie recommendation engine.
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
This repository contains code how to build job recommendation engine using Kaggle 'Job Recommendation Challenge' dataset
[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
Implementing Content based and Collaborative filtering(with KNN, Matrix Factorization and Neural Networks) in Python
A simple content-based recommender implemented in javascript
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
This is the practice of making movie recommendation engines.
Grocery Recommendation on Instacart Data
Basic of Recommendation Models
Simple Recommender System for Viblo Website using LDA (Latent Dirichlet Allocation)
Bulbul is a Music Recommendation and Streaming Platform that uses graph algorithms to provide highly personalized music recommendation and music discovery system
A react native(UI), FastAPI (Server) and MySQL(DB) non-fungible token market place with a machine learning content-based filtering recommendation engine.
This is a book recommendation engine built using a hybrid model of Collaborative filtering, Content Based Filtering and Popularity Matrix.
Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)
Implemented Content-based filtering, Collaborative filtering and K-Means Clustering on MovieLens Dataset(https://www.kaggle.com/rounakbanik/the-movies-dataset/data)
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