https://github.com/dmlc/experimental-mf

cache-friendly multithread matrix factorization
https://github.com/dmlc/experimental-mf

Last synced: 2 months ago
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cache-friendly multithread matrix factorization

• Host: GitHub
• URL: https://github.com/dmlc/experimental-mf
• Owner: dmlc
• License: apache-2.0
• Created: 2015-05-28T18:19:20.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2016-06-29T04:40:19.000Z (almost 9 years ago)
• Last Synced: 2025-03-29T15:04:36.076Z (3 months ago)
• Language: C++
• Size: 59.6 KB
• Stars: 88
• Watchers: 22
• Forks: 32
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Fast Matrix Factorization

## Features

   * Cache-friendly Multithread Matrix Factorization.

   * Fast Multithread Stochastic Gradient Langevin Dynamics (SGLD) for Matrix Factorization.

   * Fast Multithread Differentially Private Matrix Factorization.

   * Matrix Factorization with Adaptive Regularizer.

## Data

We support Google's Protobuf as input. Try data/getdata.cc to convert from userwise raw data to protobuf format:

    ./getdata -r [userwise_raw_data] -w [protobuf_binary] --method [protobuf] --size [int]

  A sample of userwise raw data looks like:

    0:

    11,5.0

    21,3.0

    1:

    9,5.0

    12,1.0

  where there are two users '0' and '1'.

  Or if you only have rating wise raw data, you can first convert to a userwise raw data:

    ./getdata -r [rating_wise_raw] -w [userwise_raw_data] --method [userwise] --split [int]

  A sample of rating_wise_raw data with a header looks like:

    100000

    0,1,5.0

    0,2,1.0

  where the header indicates the number of ratings, and follows by user_id, item_id and rating in each line.

## Environment Requirment

  * GCC 4.9 or higher

    ```bash

    tar zxf gcc-4.9.2.tar.gz;cd gcc-4.9.2;contrib/download_prerequisites;cd ..;mkdir buildc;cd buildc;../gcc-4.9.2/configure --disable-multilib;make -j 32;sudo make install;cd ..;

    ```

  * Intel TBB

    ```bash

    sudo apt-get install libtbb-dev

    ```

  * Google Protobuf

    ```bash

    sudo apt-get install -y libprotobuf-dev; sudo apt-get install -y protobuf-compiler;

    ```

  * Intel MKL

## Reference

[1] [Fast Differentially Private Matrix Factorization](http://arxiv.org/abs/1505.01419). Ziqi Liu, Yu-Xiang Wang, Alex Smola.

[2] Learning recommender systems with adaptive regularization. Steffen Rendle.