3 repositorios
Recommendation strategies that use structured knowledge graphs to discover relationships between entities.
Distinct from Knowledge Graph Builders: Focuses on using the graph for generating recommendations rather than the construction of the graph itself.
Explore 3 awesome GitHub repositories matching data & databases · Knowledge Graph Recommendations. Refine with filters or upvote what's useful.
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
Generates recommendations by leveraging structured knowledge graph data to explore entity relationships.
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
Incorporates structured external knowledge from knowledge graphs to enhance item discovery and preference prediction.
OpenKE es un framework de embedding de grafos de conocimiento diseñado para transformar grafos de conocimiento estructurados en representaciones vectoriales de baja dimensión. Funciona como una biblioteca para el aprendizaje de representaciones y un conjunto de herramientas para convertir entidades y relaciones en embeddings numéricos. El proyecto incluye un motor de predicción de enlaces para evaluar la probabilidad de relaciones entre entidades e identificar hechos faltantes en grafos a gran escala. Proporciona una herramienta de preprocesamiento dedicada para mapear cadenas de entidades y relaciones sin procesar en identificadores numéricos para el entrenamiento de aprendizaje automático. Las capacidades del framework cubren el ciclo de vida completo del embedding de grafos, incluyendo el preprocesamiento de datos, el aprendizaje de representaciones y el análisis de predicción de enlaces.
Identifies missing facts by analyzing the likelihood of relationships between entities in a structured graph.