A collection of resources for Recommender Systems (RecSys)
- Basic of Recommender Systems
- Nearest Neighbor Search
- Classic Matrix Facotirzation
- Singular Value Decomposition (SVD)
- SVD++
- Content-based CF / Context-aware CF
- there are so many ...
- Advanced Matrix Factorization
- Factorization Machine
- Sparse LInear Method (SLIM)
- Learning to Rank
- Cold-start
- Network Embedding
- Sequential-based
- Factorizing Personalized Markov Chains for Next-Basket Recommendation
- [Learning Hierarchical Representation Model for NextBasket Recommendation]
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation]
- Session-based Recommendations with Recurrent Neural Networks
- [Self-Attentive Sequential Recommendation]
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer]
- [S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization]
- [Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation]
- CapsNet-based
- [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall]
- [Controllable Multi-Interest Framework for Recommendation]
- Translation Embedding
- Graph-Convolution-based
- Knowledge-Graph-based
- Collaborative knowledge base embedding for recommender systems
- Knowledge Graph Convolutional Networks for Recommender Systems
- KGAT: Knowledge Graph Attention Network for Recommendation
- Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
- Ripplenet: Propagating user preferences on the knowledge graph for recommender systems
- Rating-Prediction-based
- Joint Deep Modeling of Users and Items Using Reviews for Recommendation
- Neural Attentional Rating Regression with Review-level Explanations
- Convolutional Matrix Factorization for Document Context-Aware Recommendation
- A Context-Aware User-Item Representation Learning for Item Recommendation
- DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation
- Muti-task Learning
- Deep Learning (early DL research)
- Deep Neural Networks for YouTube Recommendations
- Deep Learning based Recommender System: A Survey and New Perspectives
- Neural Collaborative Filtering
- Collaborative Deep Learning for Recommender Systems
- Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
- Collaborative recurrent autoencoder: recommend while learning to fill in the blanks
- TensorFlow Wide & Deep Learning
- Deep Neural Networks for YouTube Recommendations
- Collaborative Memory Network for Recommendation Systems
- Variational Autoencoders for Collaborative Filtering
- Neural Graph Collaborative Filtering
- Recommender Systems Datasets
- GroupLens
- Amazon Product Data
- Books, Electronics, Movies, etc.
- SNAP Datasets
- #nowplaying Dataset
- Last.fm Datasets
- Million Song Dataset
- Frappe
- Yahoo! Webscope Program
- music ratings, movie ratings, etc.
- Yelp Dataset Challenge
- MovieTweetings
- Foursquare
- Epinions
- Google Local
- location, phone number, time, rating, addres, GPS, etc.
- CiteUlike-t
- LibimSeTi
- Scholarly Paper Recommendation Datasets
- Netflix Prize Data Set
- FilmTrust,CiaoDVD
- Chicago Entree
- Douban
- BibSonomy
- Delicious
- Foursquare
- SmartMedia Adressa News Dataset
- MACLab LJ Datasets
- Kaggle::Datasets
- UCSD Book Graph
- libFM - Factorization Machine Library
- fastFM - A Library for Factorization Machines
- LIBFFM - A Library for Field-aware Factorization Machines
- lightfm - A Python implementation of LightFM, a hybrid recommendation algorithm
- LIBMF - A Matrix-factorization Library for Recommender Systems
- LibRec - A Leading Java Library for Recommender Systems
- LensKit - Open-Source Tools for Recommender Systems
- Surprise - A Python scikit building and analyzing recommender systems
- MyMediaLite Recommender System Library
- QMF - A matrix factorization library
- proNet-core - A general-purpose network embedding framework: pair-wise representations optimization Network
- Rival - An open source Java toolkit for recommender system evaluation
- TensorRec - A TensorFlow recommendation algorithm and framework in Python
- OpenRec - An open-source and modular library for neural network-inspired recommendation algorithms
- spotlight - Deep recommender models using PyTorch.
- Recoder - Large scale training of factorization models for Collaborative Filtering with PyTorch.
- Ranking - TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform.
- RecNN - Reinforced Recommendation toolkit build around pytorch 1.4
- recommenders - This repository contains examples and best practices for building recommendation systems.
- Precision and Recall
- Mean Average Precision (MAP)
- ROC Curve / Area under the curve
- Normalized Discounted Cumulative Gain (NDCG)
- Mean Absolute Error (MAE)
- Root Mean Square Error (RMSE)
- Novelty and Diversity
- Beyond accuracy
- List of Recommender Systems - A List of Recommender Systems and Resources
- Recommendation and Ratings Public Data Sets For Machine Learning
- RecommenderSystem-Paper
- Must-read papers on Recommender System
- knowledge graph, user-item profile, recommendation system
- Must-read Papers on Recommendation System and CTR Prediction
- Recommender Systems Specialization, University of Minnesota
- Introduction to Recommender Systems: Non-Personalized and Content-Based, University of Minnesota
- Kaggle - product recommendations, hotel recommendations, job recommendations, etc.
- ACM RecSys Challenge
- WSDM Cup 2018
- KDD Cup 2020 Challenges
- Million Song Dataset Challenge
- Netflix Prize