# LightLDA

**Repository Path**: billy_liu/LightLDA

## Basic Information

- **Project Name**: LightLDA
- **Description**: Scalable, fast, and lightweight system for large-scale topic modeling
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-24
- **Last Updated**: 2024-07-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# LightLDA

LightLDA is a distributed system for large scale topic modeling. It implements a distributed sampler that enables very large data sizes and models. LightLDA improves sampling throughput and convergence speed via a fast O(1) metropolis-Hastings algorithm, and allows small cluster to tackle very large data and model sizes through model scheduling and data parallelism architecture. LightLDA is implemented with C++ for performance consideration.

We have sucessfully trained big topic models (with trillions of parameters) on big data (Top 10% PageRank values of Bing indexed page, containing billions of documents) in Microsoft. For more technical details, please refer to our [WWW'15 paper](http://www.www2015.it/documents/proceedings/proceedings/p1351.pdf). 

For documents, please view our website [http://www.dmtk.io](http://www.dmtk.io).

## Why LightLDA

The highlight features of LightLDA are

* **Scalable**: LightLDA can train models with trillions of parameters on big data with billions of documents, a scale previous implementations cann't handle. 
* **Fast**: The sampler can sample millions of tokens per second per multi-core node.
* **Lightweight**: Such big tasks can be trained with as few as tens of machines.

## Quick Start

Run ``` $ sh build.sh ``` to build lightlda.
Run ``` $ sh example/nytimes.sh ``` for a simple example.


## Reference

Please cite LightLDA if it helps in your research:

```
@inproceedings{yuan2015lightlda,
  title={LightLDA: Big Topic Models on Modest Computer Clusters},
  author={Yuan, Jinhui and Gao, Fei and Ho, Qirong and Dai, Wei and Wei, Jinliang and Zheng, Xun and Xing, Eric Po and Liu, Tie-Yan and Ma, Wei-Ying},
  booktitle={Proceedings of the 24th International Conference on World Wide Web},
  pages={1351--1361},
  year={2015},
  organization={International World Wide Web Conferences Steering Committee}
}
```

Microsoft Open Source Code of Conduct
------------

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.