Awesome GitHub RepositoriesCollaborative Filtering Models

Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimiza

Recommenders is a recommendation system framework designed for building, benchmarking, and deploying collaborative and content-based filtering models. It provides a machine learning model pipeline that standardizes the process of moving recommendation data from raw ingestion through training and evaluation. The project functions as a model benchmarking toolkit, utilizing standardized ranking and error metrics to compare the accuracy of different algorithms. It also serves as a hyperparameter tuning tool, allowing for the optimization of model behavior and performance via external configuratio

This project is a recommendation system framework designed for building, evaluating, and operationalizing personalized item suggestion engines. It provides a comprehensive toolkit for implementing collaborative filtering and content-based algorithms, supported by an end-to-end machine learning pipeline for preparing datasets and deploying predictive models. The framework distinguishes itself through the integration of knowledge graphs to provide richer context for recommendations and the use of industry-specific patterns to accelerate system deployment. It also includes a specialized model ev

Gorse is a personalized recommendation engine server and machine learning pipeline designed to suggest items to users based on their behavior and preferences. It operates as a distributed system that separates training, candidate generation, and serving nodes to support high-throughput workloads. The system utilizes a multi-stage recommendation pipeline to refine results through retrieval, scoring, and reranking. It generates personalized suggestions using collaborative filtering, matrix factorization, and item-to-item similarity models, while also providing non-personalized and fallback reco

This project is a machine learning implementation library featuring a collection of code examples that implement supervised, unsupervised, and reinforcement learning algorithms from scratch. It provides a comprehensive set of toolkits for core machine learning components, including a natural language processing toolkit, a reinforcement learning framework, and suites for data dimensionality reduction and pattern mining. The library includes specialized implementations for reinforcement learning, such as Q-Learning, Deep Q-Networks, and Actor-Critic agents. The natural language processing capab

Surprise is a Python library for building and analyzing recommendation systems. It provides a comprehensive toolkit for implementing collaborative filtering to predict user preferences and generate item suggestions based on historical rating patterns. The library includes dedicated tools for hyperparameter optimization and model evaluation. It allows for searching through parameter sets to find the most effective configurations and utilizes a suite of metrics to measure prediction accuracy. The framework covers the full development workflow, including data loading from various sources, the c

RecBole ist ein PyTorch-basiertes Empfehlungs-Framework, das für das Erstellen, Trainieren und Evaluieren einer Vielzahl von Empfehlungsalgorithmen entwickelt wurde. Es dient als standardisierte Benchmark-Umgebung, die den Vergleich verschiedener Modellarchitekturen unter Verwendung öffentlicher Datensätze und konsistenter Evaluierungsmetriken ermöglicht. Das Projekt bietet spezialisierte Toolkits für sequenzielle Empfehlungen und Knowledge-Graph-Integration, was die Vorhersage von Item-Sequenzen basierend auf der Nutzerhistorie oder die Einbindung von strukturiertem externem Wissen ermöglicht. Es enthält eine dedizierte Hyperparameter-Optimierungs-Engine, die Grid-Search und Bayesian-Optimierung nutzt, um Modellkonfigurationen abzustimmen. Das Framework deckt ein breites Spektrum an Funktionen ab, einschließlich Datenmanagement zur Standardisierung von Interaktionslogs, Trainings-Pipelines mit verteilter Gradienten-Synchronisierung und Mixed-Precision-Ausführung sowie umfassende Evaluierungstools für Kandidaten-Ranking und Diversitätsanalyse. Es unterstützt mehrere Empfehlungstypen, wie allgemeines Collaborative Filtering und Click-Through-Rate-Vorhersage. Die Bibliothek ist in Python implementiert und nutzt PyTorch als zugrunde liegendes Empfehlungs-Framework.

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen