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 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 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 models
• Real-time incremental updates for many recommenders

Changes to previous version:

There are many new features:

• A prototype SOAP web service for rating prediction. We plan to offer most of MyMediaLite's functionality using RESTful services in the future.
• New recommenders: FactorWiseMatrixFactorization, BiasMatrixFactorizationMAE
• Simplified interfaces IRatingPrediction and IItemRecommender
• The library (and the command-line tools) now also offer an online evaluation protocol that uses incremental updates.

Important announcement: This may be the last version to support Mono 2.6. If you use MyMediaLite and rely on running it on Mono 2.6, please tell us so that we can figure out how you will be able to run future versions of MML.

See the Changes file for details and further improvements.

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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