Project details for MyMediaLite

Screenshot MyMediaLite 1.99

by zenog - October 8, 2011, 20:16:05 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, 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:
  • MyMediaLite now needs Mono 2.8 or later, 2.10.x is recommended, or .NET 4.0
  • multi-core support for several components: item recommendation evaluation; cross-validation for rating prediction and item recommendation; MultiCoreMatrixFactorization: distributed SGD training for rating prediction matrix factorization
  • support long int user and item IDs
  • evaluation improvements: different modes for selecting candidate items now also work for cross-validation; new evaluation measure: mean reciprocal rank (MRR); move item recommender evaluation measures into their own classes; move online evaluation and cross-validation for both items and ratings into their own classes, respectively
  • numerous improvements in documentation, API, command-line tools, and helper scripts
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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