CoreRec offers a robust recommendation system based on graph analysis. It can recommend similar nodes within a graph, aiding in various applications such as personalized recommendations in social networks or product recommendations in e-commerce platforms.
Features
Advanced Graph Analysis
Provides cutting-edge tools for analyzing complex graph structures, ideal for data scientists and researchers.
Node Recommendation Engine
Powerful engine to recommend similar nodes within a graph, enhancing user experience and engagement.
Customizable Transformer Model
Define and train Transformer models tailored to your graph data with customizable parameters for optimal performance.
PyTorch Dataset Integration
Seamlessly integrate graph data with PyTorch datasets, streamlining the model training process.
Flexible Model Training
Train your models with ease using CoreRec's flexible training functions, supporting various configurations.
Accurate Recommendation Metrics
Measure the accuracy of recommendations with robust metrics provided by CoreRec.
2D Graph Visualizations
Create stunning 2D visualizations of graphs, making data analysis more intuitive and insightful.
3D Graph Visualizations
Experience graphs in 3D with customizable features, providing a deeper understanding of complex networks.
Content-Based Filtering
Incorporates traditional machine learning algorithms, neural network models, and hybrid approaches to recommend items based on content.
Collaborative Filtering
Focuses on methods like matrix factorization and neural networks to make recommendations based on user-item interactions.
Hybrid Recommendation Strategies
Combines multiple recommendation strategies to improve the accuracy and robustness of recommendations.
Modular and Extensible Design
Allows developers to integrate and experiment with different algorithms and techniques easily, with each submodule usable independently.