PREA: Personalized Recommendation Algorithms Toolkit

 
  1.  本文介绍了一款基于Java的面向Recommender System的开源toolkit
  2.  Toolkit主页 http://prea.gatech.edu/index.html
  3.  可用命令行setup,然后利用command运行算法(注意:数据要另行下载!命令行加入数据文件时要用绝对路径,且不要后缀
  4.  Dataset(ratings评分数据,是否可用于隐式反馈数据?)

PREA gets ARFF format of input files. You can download sample datafiles in this section. Netflix dataset is a subset of the Netflix original dataset, taken from the first part. MovieLens dataset is exactly same with the original one.

Note that time-dependent split files, using most recent ratings as test set while others as train set, are available only for Netflix data. For example, "netflix_3m1k_split_20050701.txt" uses ratings after 2005/07/01 as test set while all previous ones as train set. The idea is detailed in Gunawardana and Shani, JMLR 2009.

Toolkit实现的算法如下: (算法描述)

Algorithm Keyword Parameter List
Example
Constant const java prea/main/Prea -a const
Overall Average allavg java prea/main/Prea -a allavg
User Average useravg java prea/main/Prea -a useravg
Item Average itemavg java prea/main/Prea -a itemavg
Random random java prea/main/Prea -a random
User-based CF userbased java prea/main/Prea -a userbased [neighbor size(k)] [similarity method]* [(optional) default] [default value] [(optional) usersim]** [user similarity prefetch file name]
java prea/main/Prea -a userbased 50 pearson
java prea/main/Prea -a userbased 50 pearson default 3.0
java prea/main/Prea -s pred netflix_3m1k_split.txt -a userbased 50 mad default 3.0 usersim netflix_3m1k_userSim.txt
Item-based CF itembased java prea/main/Prea -a itembased [neighbor size(k)] [similarity method]* [(optional) default] [default value] [(optional) itemsim]** [item similarity prefetch file name]
java prea/main/Prea -a itembased 50 cosine
java prea/main/Prea -a itembased 50 cosine default 3.0
java prea/main/Prea -s pred netflix_3m1k_split.txt -a itembased 50 msd default 3.0 itemsim movielens_1M_itemSim.txt
Slope One slopeone java prea/main/Prea -a slopeone
Regularized SVD regsvd java prea/main/Prea -a regsvd [feature count] [learning rate] [regularizer] [max iteration]
java prea/main/Prea -a regsvd 10 0.005 0.1 200
NMF nmf java prea/main/Prea -a nmf [feature count] [regularizer] [max iteration]
java prea/main/Prea -a nmf 100 0.0001 5
PMF pmf java prea/main/Prea -a pmf [feature count] [learning rate] [regularizer] [momentum] [max iteration]
java prea/main/Prea -a pmf 10 50 0.4 0.8 20
Bayesian PMF bpmf java prea/main/Prea -a bpmf [feature count] [max iteration]
java prea/main/Prea -a bpmf 2 20
Nonlinear PMF nlpmf java prea/main/Prea -a nlpmf [feature count] [learning rate] [momentum] [max iteration] [kernel inverse width] [kernel variance RBF] [kernel variance bias] [kernel variance white]
java prea/main/Prea -a nlpmf 10 0.0001 0.9 2 1 1 0.11 5
Fast NPCA npca java prea/main/Prea -a npca [validation ratio] [max iteration]
java prea/main/Prea -a npca 0.15 5
Rank-based CF rank java prea/main/Prea -a rank [kernel width]
java prea/main/Prea -a rank 1.0
Singleton Global LLORMA sgllorma java prea/main/Prea -a sgllorma [feature count] [learning rate] [regularizer] [max iteration] [model count]
java prea/main/Prea -a sgllorma 5 0.1 0.001 100 50
Singleton Parallel LLORMA spllorma java prea/main/Prea -a spllorma [feature count] [learning rate] [regularizer] [max iteration] [model count] [thread count]
java prea/main/Prea -a spllorma 10 0.01 0.001 100 50 8
Rank-based SVD ranksvd java prea/main/Prea -a ranksvd [loss code]*** [local model rank] [learning rate]
java prea/main/Prea -a ranksvd log_mult 1 1500
Paired Global LLORMA pgllorma java prea/main/Prea -a pgllorma [loss code]*** [local model count] [local model rank] [learning rate]
java prea/main/Prea -a pgllorma log_mult 5 1 1500

 



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