We have collection of more than 1 Million open source products ranging from Enterprise product to small libraries in all platforms. We aggregate information from all open source repositories. Search and find the best for your needs. Check out projects section.
implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets
Fast Python Collaborative Filtering for Implicit Datasets. Alternating Least Squares as described in the papers Collaborative Filtering for Implicit Feedback Datasets and Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering.
collaborative-filtering machine-learning matrix-factorization recommender-systemRecommenders - Recommender Systems
Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting train/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on Spark, or on Azure Databricks.
recommender-system machine-learning-algorithms ranking recommendation-algorithmsseldon-server - Machine Learning Platform and Recommendation Engine built on Kubernetes
This project is not actively maintained anymore please see Seldon Core. Seldon Server is a machine learning platform that helps your data science team deploy models into production.
machine-learning deep-learning deployment kubernetes docker microservices spark kafka kafka-streams tensorflow cloud aws gcp azure seldon recommender-system recommendation-engine predictionspotlight - Deep recommender models using PyTorch.
Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models. See the full documentation for details.
recommender-system deep-learning learning-to-rank machine-learning matrix-factorization pytorchlightfm - A Python implementation of LightFM, a hybrid recommendation algorithm.
LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features).
machine-learning recommender matrix-factorization learning-to-rank recommender-systemawesome-DeepLearning - 深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI
深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI
nlp video reinforcement-learning detection cnn transformer gan dqn classification rnn sarsa segmentation recommender-system bert pose dssm tinybert dynabertAlink - Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform
Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
machine-learning data-mining statistics kafka graph-algorithms clustering word2vec regression xgboost classification recommender recommender-system apriori feature-engineering flink fm flink-ml flink-machine-learningrecommenders - TensorFlow Recommenders is a library for building recommender system models using TensorFlow
TensorFlow Recommenders is a library for building recommender system models using TensorFlow. It helps with the full workflow of building a recommender system: data preparation, model formulation, training, evaluation, and deployment.
tensorflow recommender recommender-system tensorflow-recommendersAd-papers - Papers on Computational Advertising
Papers on Computational Advertising
machine-learning deep-learning recommender-system advertising papers ctr-prediction computational-advertisingReco-papers - Classic papers and resources on recommendation
Classic papers and resources on recommendation
machine-learning reinforcement-learning deep-learning recommender-system recommendation exploration-exploitationSparrowRecSys - A Deep Learning Recommender System
A Deep Learning Recommender System
machine-learning deep-learning recommender-systemcatalyst - Accelerated deep learning R&D
Accelerated deep learning R&D
infrastructure machine-learning natural-language-processing information-retrieval research reinforcement-learning computer-vision deep-learning text-classification distributed-computing image-processing pytorch image-classification metric-learning recommender-system object-detection image-segmentation reproducibility text-segmentationQdrant - Neural Search Engine, Vector Similarity Search Engine with extended filtering support
Qdrant ( quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.
search-engine elasticsearch neural-network matching filter saas nearest-neighbor-search image-search recommender-system vectors approximate-nearest-neighbor-search knn-algorithm hnsw vector-search vector-search-engine embeddings-similarity semantic-searchtensorrec - A TensorFlow recommendation algorithm and framework in Python.
A TensorFlow recommendation algorithm and framework in Python. TensorRec is a Python recommendation system that allows you to quickly develop recommendation algorithms and customize them using TensorFlow.
machine-learning tensorflow recommendation-system recommender-system recommendation-algorithm frameworkfastFM - fastFM: A Library for Factorization Machines
The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citations of the software. If you publish scientific articles using fastFM, please cite the following article (bibtex entry citation.bib). This repository allows you to use Factorization Machines in Python (2.7 & 3.x) with the well known scikit-learn API. All performance critical code as been written in C and wrapped with Cython. fastFM provides stochastic gradient descent (SGD) and coordinate descent (CD) optimization routines as well as Markov Chain Monte Carlo (MCMC) for Bayesian inference. The solvers can be used for regression, classification and ranking problems. Detailed usage instructions can be found in the online documentation and on arXiv.
machine-learning recommender-system factorization-machines matrix-factorizationrecsim - A Configurable Recommender Systems Simulation Platform
RecSim is a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration. For a detailed description of the RecSim architecture please read Ie et al. Please cite the paper if you use the code from this repository in your work. This is not an officially supported Google product.
machine-learning google reinforcement-learning simulation tensorflow artificial-intelligence recommender-systembuffalo - TOROS Buffalo: A fast and scalable production-ready open source project for recommender systems
Buffalo is a fast and scalable production-ready open source project for recommender systems. Buffalo effectively utilizes system resources, enabling high performance even on low-spec machines. The implementation is optimized for CPU and SSD. Even so, it shows good performance with GPU accelerator, too. Buffalo, developed by Kakao, has been reliably used in production for various Kakao services. This software is licensed under the Apache 2 license, quoted below.
machine-learning cpp matrix-factorization recommender-systemwinerama-recommender-tutorial - A wine recommender system tutorial using Python technologies such as Django, Pandas, or Scikit-learn, and others such as Bootstrap
This repository contains the code for a wine reviews and recommendations web application, in different stages as git tags. The idea is that you can follow the tutorials through the tags listed below, and learn the different concepts explained in them. The tutorials include instructions on how to deploy the web using a Koding account. However, Koding recently moved from solo to team accounts and the link provided to my Koding account deployment of the tutorial result is not working anymore. The tutorial can still be followed with no problem at all. The following tutorials will guide you through each of the previous Git tags while learning different concepts of data product development with Python.
tutorial django scikit-learn pandas recommender-system wineRecommendation Engine Demo
How does the Amazon recommendation works? This is about visualizing the item to item collaborations filtering mechanism using a item-to-item matrix table. The item-to-item matrix, the vectors and the calculated data values are displayed. There are n different items and...
engine recommendation recommender-systemawesome-RecSys-papers - The awesome and classic papers in recommendation system!!! Good luck to every RecSys-learner!
The topic of my dissertation is recommendation system. I collected some classic and awesome papers here. Good luck to every RecSys-learner. My email is ZhangYuyang4d@163.com. If you find any mistakes, or you have some suggestions, just send a email to me.
awesome recommender-system papers
We have large collection of open source products. Follow the tags from
Tag Cloud >>
Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
Add Projects.
