Version 2.0 of MyMediaLite

Release Notes: Rating data with timestamps is now supported. There are two time-aware baselineline recommenders, TimeAwareBaseline and TimeAwareBaselineWithFrequencies (from the Netflix Grand Prize solution), and chronological splits. Some matrix functions are now implemented as extension methods, which makes them easier to use in many cases. The script download_movielens.sh has been updated because the download URLs have changed. Lots of improvements have been made under the hood.

    Other releases

    •  30 Dec 2012 19:14

      Release Notes: Important changes: fold-in support for UserKNN for item recommendation; less verbose evaluation output; and many bugfixes.

      •  24 Jul 2012 07:01

        Release Notes: The release added NaiveBayes for attribute-based rating prediction, MostPopularByItemAttributes, and improvements and bugfixes in WeightedBPRMF, SigmoidCombinedAsymmetricFactorModel, and ItemKNN. A bug in the Map@k routine was fixed. Support for Million Song Dataset Challenge data was improved.

        •  30 May 2012 23:27

          Release Notes: This release adds several new rating predictors, all of them variants of asymmetric factor models (AFMs). The new item recommender MostPopularByItemAttributes generalizes an idea presented by the organizers of the Million Song Database Challenge. There are now 27 different rating predictors and 18 different item recommenders in MyMediaLite.

          •  05 Apr 2012 12:42

            Release Notes: This release adds the new rating predictor SigmoidItemAsymmetricFactorModel, improvements and bugfixes for SocialMF, speed-ups for ItemKNN rating predictors, faster writing of item recommendations, and fast (de)serialization of data sets.

            •  03 Mar 2012 14:45

              Release Notes: Float (32-bit) is now used instead of double (64-bit) to store ratings and model parameters. The incremental update API now accepts several feedback events at once. A new SVD++ rating predictor was added. LogisticRegressionMatrixFactorization and MultiCoreMatrixFactorization were merged into BiasedMatrixFactorization. There were many small enhancements and fixes, and polishing.

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