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, NMAE, 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 command-line tools now use reflection to automagically find all relevant recommenders. This means you do not have to modify the command-line tools any more to use your newly implemented recommenders! The kNN-based methods are now faster and consume less memory because they take data sparsity better into account. A method for the diversification of result sets has been added to the experimental section of MyMediaLite. Some namespaces and types have been renamed to have nicer, more intuitive names.

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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