Project details for MyMediaLite

Screenshot MyMediaLite 3.04

by zenog - October 16, 2012, 14:28:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms.

It addresses the two most common scenarios in collaborative filtering:

  • rating prediction (e.g. on a scale of 1 to 5 stars), and
  • item recommendation from positive-only feedback (e.g. from clicks or purchase actions).

MyMediaLite gives you a choice of many recommendation methods:

  • dozens of different recommenders
  • methods can use collaborative and attribute/content data

MyMediaLite is ready to use:

  • MyMediaLite includes evaluation routines for rating prediction and item prediction; it can measure MAE, NMAE, RMSE, CBD, AUC, prec@N, MAP, NDCG, MRR.
  • It also comes with command line tools for both recommendation tasks that read a simple text-based files.

MyMediaLite is compact: The core library has a size of about 150KB.

Portability: Written in C#, for the .NET platform; runs on every architecture supported by Mono: Linux, Windows, Mac OS X.

Freedom: MyMediaLite is free software/open source software. It can be used, modified, and distributed under the terms of the GNU General Public License (GPL).

Additional features:

  • Serialization: save and reload recommender models
  • Real-time incremental updates for many recommenders
  • multi-core support
Changes to previous version:
  • kNN recommenders: bug fix for incremental updates when using asymmetric correlations
  • ItemKNN: fold-in rating prediction support
  • matrix factorization models: multiplicative learning rate decay
  • improved API doc for MatrixFactorization, BPRMF, and UserItemBaseline
  • WRMF: rename hyperparameter c_pos to alpha for better consistency with the original paper by Hu et al.
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Linux, Windows, Solaris, Mac Os X
Data Formats: Csv, Tab Separated, Sql
Tags: Gradient Based Learning, Large Scale Learning, Algorithms, Data Mining, Evaluation, Supervised Learning, Collaborative Filtering, Matrix Factorization, Recommender Systems, Knn, Library, Dotnet, Mono
Archive: download here


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