CoreRec excels in node recommendations, model training, and graph visualizations, making it the ultimate tool for data scientists and researchers.
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.
CoreRec provides cutting-edge tools for analyzing complex graph structures, making it ideal for data scientists and researchers.
Utilize CoreRec's powerful engine to recommend similar nodes within a graph, enhancing user experience and engagement.
Define and train Transformer models tailored to your graph data with customizable parameters for optimal performance.
Seamlessly integrate graph data with PyTorch datasets, streamlining the model training process.
Train your models with ease using CoreRec's flexible training functions, supporting various configurations.
Measure the accuracy of your recommendations with robust metrics provided by CoreRec.
Create stunning 2D visualizations of your graphs, making data analysis more intuitive and insightful.
Experience your graphs in 3D with customizable features, providing a deeper understanding of complex networks.
A comprehensive suite of tools for researchers and developers.
Sophisticated node recommendation engine powered by advanced graph algorithms.
Build and train bespoke Transformer & GNN models tailored to your specific data.
Optimized for speed with support for Data Parallelism processing for large datasets.
Seamlessly integrate your graph data with PyTorch datasets for streamlined workflows.
Generate insightful 2D and 3D graph visualizations to better understand your data.
Evaluate your model's performance with a robust suite of recommendation metrics.
Explore the core functionalities of CoreRec with these examples.
A simple example to get you started with loading data and preparing user interactions for analysis.
import corerec as cr
# Load data from specified paths
users = cr.load_users('data/users.dat')
ratings = cr.load_ratings('data/ratings.dat')
movies = cr.load_movies('data/movies.dat')
# Build user-item interaction matrix
user_interactions = cr.build_user_interactions(ratings)
print("User interactions created successfully.")
Leverage contextual information like time of day or location to provide highly relevant recommendations.
import corerec as cr
# Pre-computation of item features
item_features = cr.build_item_features(movies)
# Initialize and fit the context-aware model
recommender = cr.CON_CONTEXT_AWARE(
context_config_path='data/context_config.json',
item_features=item_features
)
recommender.fit(user_interactions)
# Get recommendations for a specific user and context
recs = recommender.recommend(user_id=1, context={'time': 'evening'}, top_n=5)
print(recs)
Use a Convolutional Neural Network to learn features from item content for recommendations.
import torch
from corerec.cr_utility.dataloader import DataLoader as CRDataLoader
from corerec.engines.contentFilterEngine.nn_based_algorithms import NN__CNN
# Assuming a custom PyTorch dataset `MoviesDataset`
dataset = MoviesDataset(file_path='data/movies.dat')
dataloader = CRDataLoader(dataset, batch_size=32, shuffle=True)
# Initialize the model, loss, and optimizer
model = NN__CNN(input_dim=20, num_classes=5)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# Training loop
train_model(model, dataloader, criterion, optimizer, num_epochs=10, device='cpu')
Implement state-of-the-art sequence models for powerful session-based recommendations.
from corerec.engines.contentFilterEngine.nn_based_algorithms import NN__TransformerModel
# Assuming a custom PyTorch dataset `MoviesDataset`
dataset = MoviesDataset(file_path='data/movies.dat')
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# Initialize Transformer model
transformer_model = NN__TransformerModel(input_dim=500, nhead=5, num_layers=2)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.Adam(transformer_model.parameters(), lr=0.001)
# Train the model
train_model(transformer_model, dataloader, criterion, optimizer, num_epochs=5, device='cpu')
CoreRec and VishGraphs are completely open source projects. We highly support and encourage open source contributions. You are free to use, modify, and distribute the code as long as you adhere to the terms of the open source license.
[Tip for developers]: If you find this project useful, consider contributing back to the community by submitting bug fixes, feature enhancements, or documentation improvements.
If you want to support the development of these open source projects, you can star the repository on GitHub. Thank you for your support!
Have a project in mind, a question about CoreRec, or just want to connect? Drop me a line.