Project details for MyMediaLite

Screenshot MyMediaLite 1.05

by zenog - September 1, 2011, 22:26:43 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 prediction from implicit 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, AUC, prec@N, MAP, NDCG.
  • It also comes with command line tools for both recommendation tasks that read a simple text-based input format.

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
Changes to previous version:

API changes:

  • ISplit: use IList instead of List for Train and Test; NumberOfFolds is now a uint instead of an int
  • Eval.Ratings and Eval.Items: replace DisplayResults by FormatResults new feature: recommendations for groups

new recommenders:

  • WeightedBPRMF (used for KDD Cup 2011, track 2)
  • SoftMarginRankingMF (item recommender inspired both by CofiRank, ranking loss, and BPR-MF, stochastic gradient descent learning)

evalation: k-fold cross-validation protocol for item prediction

numerous improvements in documentation, command-line tools, helper scripts

See the Changes file for details and further improvements.

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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