Recommendation Algorithm大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、AITM，DSIN，SIGN，IPREC、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、ESCMM, MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise、DeepRec、ENSFM，TiSAS，AutoFIS等，包含经典推荐系统数据集criteo 、movielens等

MovieLens based recommender system.使用MovieLens数据集训练的电影推荐系统。

An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

4 different recommendation engines for the MovieLens dataset.

An example of doing MovieLens recommendations using triplet loss in Keras

基于MovieLens-1M数据集实现的协同过滤算法demo

Download and preprocess popular sequential recommendation datasets

Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

A step-by-step tutorial on developing a practical recommendation system (retrieval and ranking) using TensorFlow Recommenders and Keras.

A pure Python implement of Collaborative Filtering based on MovieLens' dataset.

Python Implementation of Probabilistic Matrix Factorization(PMF) Algorithm for building a recommendation system using MovieLens ml-100k | GroupLens dataset

🍃 Recommender System in JavaScript for the MovieLens Database

Knowledge-based, Content-based and Collaborative Recommender systems are built on MovieLens dataset with 100,000 movie ratings. These Recommender systems were built using Pandas operations and by fitting KNN, SVD & deep learning models which use NLP techniques and NN architecture to suggest movies for the users based on similar users and for queries specific to genre, user, movie, rating, popularity.

matlab, collaborative filtering, MovieLens dataset，The movie recommendation system

A set of Jupyter notebooks demonstrating collaborative filtering using matrix factorization with Keras.

使用MovieLens数据集实现了基于Auto Encoder(AE), Variational Auto Encoder(VAE), BERT的深度学习电影推荐系统

IMDb URLs and posters for movies in the MovieLens 100K dataset

利用MovieLens数据，Pearson相似度，分别基于User和Item构建一个简单的kNN推荐系统，并给出RMSE评测

"Building a MovieLens Recommender System" TMLS 2020 Workshop Material

Multi-Armed Bandit algorithms applied to the MovieLens 20M dataset

a simple yet versatile recommendation systems library in python

MovieLens recommendation system using reinforcement learning (GYM + PPO)

基于movielens数据集的电影推荐系统

Recommendation Models in TensorFlow

使用 Spark MLlib 的 ALS 算法的电影推荐系统

基于movielens-25m数据集的生成式推荐项目

A repository for a machine learning project about developing a hybrid movie recommender system.

用来处理freebase, kb4rec, movielens它们数据集的项目

A movie recommendation system based on the GroupLens dataset of MovieLens data

Building recommenders with Elastic Graph!