Awesome GitHub RepositoriesImplicit Feedback Modeling

LightFM es una biblioteca de recomendación de Python y framework de machine learning diseñado para predecir las preferencias de los usuarios. Implementa un motor de recomendación híbrido que combina filtrado colaborativo con filtrado basado en contenido mediante la integración de datos de interacción usuario-ítem con metadatos descriptivos. El sistema utiliza factorización de matrices híbrida para aprender representaciones latentes de usuarios e ítems. Está diseñado específicamente para manejar retroalimentación implícita, utilizando funciones de pérdida especializadas como Weighted Approximate Rank Pairwise (WARP) y Bayesian Personalized Ranking (BPR) para optimizar las preferencias de ítems para conjuntos de datos que carecen de calificaciones negativas. La biblioteca proporciona herramientas para entrenar modelos mediante descenso de gradiente estocástico, calcular predicciones de preferencia de ítems y evaluar la precisión del modelo. Admite la clasificación personalizada de ítems y la predicción del comportamiento del usuario mediante la síntesis de matrices de interacción con embeddings de características.

RecBole is a PyTorch-based recommendation framework designed for building, training, and evaluating a wide variety of recommendation algorithms. It serves as a standardized benchmark environment that allows for the comparison of different model architectures using public datasets and consistent evaluation metrics. The project provides specialized toolkits for sequential recommendation and knowledge-graph integration, enabling the prediction of item sequences based on user history or the incorporation of structured external knowledge. It includes a dedicated hyperparameter optimization engine

Implicit is a Python recommendation engine and matrix factorization library designed for collaborative filtering. It implements predictive models that analyze implicit feedback to estimate user preferences and generate personalized item recommendations without requiring explicit ratings. The library utilizes native-code execution and multi-core parallelized processing to decompose large interaction matrices into latent factors. It incorporates approximate nearest neighbor indexing to accelerate high-dimensional similarity lookups and reduce recommendation latency. The framework covers prefer

Neural collaborative filtering is a recommendation system framework that predicts user item preferences from implicit feedback by combining generalized matrix factorization and multi-layer perceptron networks through a shared final embedding layer. It captures both linear and non-linear interactions to model user preferences from historical data. The framework executes training and evaluation runs through a configuration-driven pipeline accessible via command-line interfaces, parsing hyperparameters such as learning rates, batch sizes, and latent dimensions. It optimizes implicit feedback mod