Project details for MyMediaLite

Screenshot MyMediaLite 1.04

by zenog - August 3, 2011, 18:55:35 CET [ Project Homepage BibTeX Download ]

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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:
  • Simplified API for item recommendation and matrix factorization: nicer class names (Eval.Items and Eval.Ratings).
  • Merge MAE optimization as an option into the BiasedMatrixFactorization recommender, instead of having a separate recommender.
  • Rating prediction tool: allow user-defined formatting of prediction output.
  • Item prediction tool: allow specification of the set of items to consider for evaluation, the items in the training set (default), a given set (via --relevant-items=FILE), the ones in the test set(--test-items), or only those items in both the training and the test set --overlap-items); save time by evaluation on a random subset of the users (--num-test-users=N).
  • Fixed packaging/compilation on Windows/Microsoft .NET (thank you Artus).
  • Fixed KDD Cup examples (reported by Subramanyeshwar Cherukuri).

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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