competitive-recsys

A collection of resources for Recommender Systems (RecSys)

Recommendation Algorithms

• Basic of Recommender Systems
  • Wikipedia
• Nearest Neighbor Search
  • Wikipedia
  • sklearn.neighbors
  • Benchmarks of approximate nearest neighbor libraries
• Classic Matrix Facotirzation
  • Matrix Factorization: A Simple Tutorial and Implementation in Python
  • Matrix Factorization Techiques for Recommendaion Systems
• Singular Value Decomposition (SVD)
  • Wikipedia
• SVD++
  • Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
• Content-based CF / Context-aware CF
  • there are so many ...
• Advanced Matrix Factorization
  • Probabilistic Matrix Factorization
  • Fast Matrix Factorization for Online Recommendation with Implicit Feedback
  • Collaborative Filtering for Implicit Feedback Datasets
  • Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence
• Factorization Machine
  • Factorization Machines
  • Field-aware Factorization Machines for CTR Prediction
• Sparse LInear Method (SLIM)
  • SLIM: Sparse Linear Methods for Top-N Recommender Systems
  • Global and Local SLIM
• Learning to Rank
  • Wikipedia
  • BPR: Bayesian personalized ranking from implicit feedback
  • WSABIE: Scaling Up To Large Vocabulary Image Annotation
  • Top-1 Feedback
  • k-order statistic loss
  • VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
  • The LambdaLoss Framework for Ranking Metric Optimization
• Cold-start
  • Deep content-based music recommendation
  • DropoutNet: Addressing Cold Start in Recommender Systems
• Network Embedding
  • awesome-network-embedding
  • Item2vec
  • entity2rec
  • Collaborative Similarity Embedding for Recommender Systems
• Sequential-based
  • Factorizing Personalized Markov Chains for Next-Basket Recommendation
  • Session-based Recommendations with Recurrent Neural Networks
• Translation Embedding
  • Translation-based Recommendation
  • Translation-based Factorization Machines for Sequential Recommendation
• Graph-Convolution-based
  • GraphSAGE: Inductive Representation Learning on Large Graphs
  • PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems
  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• 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
• Deep Learning
  • 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

Public Available Datasets

• Recommender Systems Datasets
• GroupLens
  • MovieLens
  • HetRec2011
  • WikiLens
  • Book-Crossing
  • Jester
  • EachMovie
• 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
  • Steam Video Games
  • Anime Recommendations Database
• UCSD Book Graph

Open Sources

• 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.

Common Evaluation Metric

• Precision and Recall
  • Wikipedia
• Mean Average Precision (MAP)
  • Wikipedia
• ROC Curve / Area under the curve
  • Wikipedia
• Normalized Discounted Cumulative Gain (NDCG)
  • Wikipedia
• Mean Absolute Error (MAE)
  • Wikipedia
• Root Mean Square Error (RMSE)
  • Wikipedia
• Novelty and Diversity
  • Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
• Beyond accuracy
  • Beyond accuracy: evaluating recommender systems by coverage and serendipity

Related Github links

• 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

Textbooks

• Programming Collective Intelligence

Online Courses

• Recommender Systems Specialization, University of Minnesota
• Introduction to Recommender Systems: Non-Personalized and Content-Based, University of Minnesota

RecSys-related Competitions

• Kaggle - product recommendations, hotel recommendations, job recommendations, etc.
• ACM RecSys Challenge
• WSDM Cup 2018
• KDD Cup 2020 Challenges
• Million Song Dataset Challenge
• Netflix Prize

Tutorials

• RecSys tutorials
  • 2014
  • 2015
  • 2016
  • 2017
  • 2018
• Kdd 2014 Tutorial - the recommender problem revisited

Articles

• Matrix Factorization: A Simple Tutorial and Implementation in Python

• Introduction to Reinforcement Learning for News Recommendation

Conferences

• RecSys – ACM Recommender Systems