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, 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:

• MyMediaLite now needs Mono 2.8 or later, 2.10.x is recommended, or .NET 4.0
• multi-core support for several components: item recommendation evaluation; cross-validation for rating prediction and item recommendation; MultiCoreMatrixFactorization: distributed SGD training for rating prediction matrix factorization
• support long int user and item IDs
• evaluation improvements: different modes for selecting candidate items now also work for cross-validation; new evaluation measure: mean reciprocal rank (MRR); move item recommender evaluation measures into their own classes; move online evaluation and cross-validation for both items and ratings into their own classes, respectively
• numerous improvements in documentation, API, command-line tools, and helper scripts

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

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.