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 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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