Cp User Behavior
Recommendation engine using collaborative filtering and matrix factorization
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Implicit | 3,322 |  | 22 | 17 | 5 months ago | 47 | September 29, 2023 | 77 | mit | Python |
| Fast Python Collaborative Filtering for Implicit Feedback Datasets | ||||||||||
| Librec | 3,113 | 2 years ago | 79 | other | Java | |||||
| LibRec: A Leading Java Library for Recommender Systems, see | ||||||||||
| Recommendation | 324 | 2 years ago | 19 | mit | Jupyter Notebook | |||||
| Recommendation System using ML and DL | ||||||||||
| Kalman Clib | 57 | 10 months ago | mit | C | ||||||
| Microcontroller targeted C library for Kalman filtering | ||||||||||
| Kalman | 53 | 2 years ago | 5 | March 25, 2022 | mit | Go | ||||
| Adaptive Kalman filter in Golang | ||||||||||
| Recommendationengine | 37 | 6 years ago | mit | Java | ||||||
| [Deprecated] An optimized MapReduce for item‐based collaborative filtering recommendation algorithm with empirical analysis | ||||||||||
| Recommendation Systems | 28 | 3 years ago | gpl-3.0 | Jupyter Notebook | ||||||
| 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 | ||||||||||
| Recommender System | 21 | 7 years ago | Python | |||||||
| In this code we implement and compared Collaborative Filtering algorithm, prediction algorithms such as neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others. | ||||||||||
| Recommender Systems Paper | 21 | 3 years ago | ||||||||
| Must-read Papers for Recommender Systems (RS) | ||||||||||
| Conditional Build Matrix | 19 | 6 months ago | mit | JavaScript | ||||||
| A GitHub Action that enables easier conditional matrix builds! | ||||||||||
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