RecMe is a lightweight recommendation algorithm library developed using Java 1.7 + Eclipse.
The purpose of this project is to provide implementation of the state-of-the-art recommendation algorithms, mainly for self use (e.g., performance comparison with my proposed methods). Kindly send me a message if you want some recommendation algorithms to be included in the library.
Baseline: GlobalAverage, UserAverage, ItemAverage, MostPopular
Core: ItemCF, UserCF, PMF, SVD++, SocialReg (MF + social regularization)
(1) conf folder: configuration of each algorithm's parameters
(2) data folder: input dataset (e.g., MovieLens-100k)
(3) lib folder: third party libs
(4) localModels folder: saved model (e.g., learned user/item's latent factors)
(5) results folder: results of each algorithm + log information
(6) src folder: source code
Check the package "ch.epfl.lsir.xin.test" where we show how each algorithm is used to provide recommendation.
The correctness of the implemented algorithms is (in part) validated by comparing with MyMediaLite (http://mymedialite.net/) using MovieLens100k dataset. Note that rating prediction accuracy of algorithms has been thoroughly tested but the ranking performance is not fully supported yet (an important follow-up task ;).
Copyright (C) 2014 Xin Liu
RecMe: a lightweight recommendation algorithm library
RecMe is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.