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 recommendation from positive-only 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, CBD, 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:

Important changes:

• new rating predictor GSVD++ (contributed by Marcelo Manzato)
• new recommenders ExternalRatingPredictor and ExternalItemRecommender to evaluate external tools with the MyMediaLite evaluation framework
• incremental update support for item recommendation UserKNN and ItemKNN (based on a pull request by João Vinagre)
• --cross-validation support for the rating_based_ranking tool (as requested by Pieter-Jan Verbrugen)
• removed the group recommendation code
• cleaner item recommendation evaluation, with a bug fix in the cross-validation code and a complete rewrite of online evaluation
• removed unused matrix and vector math, faster and simplified matrix code

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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