Description:

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 recommender engines
• 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, RMSE, AUC, prec@N, MAP, NDCG.
• It also comes with command line tools for both tasks that read a simple text-based input format.

MyMediaLite is compact: The core library has a size of about 100KB.

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 engine models
• Real-time online updates for many recommender engines

Changes to previous version:

The new release contains many bug fixes and new features, most notably:

• also read comma-separated text files (like Mahout) and export Mahout-compatible item predictions (thanks to Damir Logar)
• Makefile-based build system allows compilation without IDE and semi-automatic release management
• scripts to automatically download MovieLens CF datasets plus movie attribute data from IMDB.com
• a portable test suite for the command line tools (should run on every Unix system)

Version 0.07 was released shortly after 0.06 to fix some packaging problems.

BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Solaris, Mac Os X

Data Formats: Csv, Tab Separated

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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