<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> Torch-RecHub is a lightweight, PyTorch-based framework designed to simplify the development and research of recommender systems. It offers a flexible and extensible solution for engineers and researchers, providing common model implementations, data processing tools, and evaluation metrics to accelerate the recommendation modeling lifecycle.

How It Works

The framework employs a modular design, enabling straightforward addition of new models, datasets, and evaluation metrics. It capitalizes on PyTorch's dynamic computation graph and GPU acceleration for efficient model training. A standardized pipeline streamlines data loading, training, and evaluation workflows, with experiments configurable via config files or command-line arguments to ensure reproducible results.

Quick Start & Requirements

Installation is straightforward via pip install torch-rechub for the stable version. For the latest features, clone the repository and use uv sync. Prerequisites include Python 3.8+, PyTorch 1.7+ (CUDA-enabled recommended), NumPy, Pandas, SciPy, and Scikit-learn. Online documentation is available in English and Chinese at https://datawhalechina.github.io/torch-rechub/ and https://datawhalechina.github.io/torch-rechub/zh/ respectively.

Highlighted Details

Torch-RecHub boasts a rich library encompassing classic and cutting-edge recommendation models. This includes matching (DSSM, MIND, GRU4Rec), ranking (WideDeep, DeepFM, DIN), multi-task learning (MMoE, ESMM, PLE), and advanced generative models (HSTU, HLLM). It also offers built-in support or preprocessing scripts for popular datasets such as MovieLens, Amazon, Criteo, and Avazu, alongside robust mechanisms for integrating custom datasets.

Maintenance & Community

The project welcomes contributions via GitHub Issues for bug reports and feature suggestions. The project lead is morningsky.

Licensing & Compatibility

The project is licensed under the MIT License, which permits commercial use and integration into closed-source projects.

Limitations & Caveats

No specific limitations or caveats are detailed in the provided README content.