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 |
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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 |
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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 |
文献题目 | 去谷歌学术搜索 | ||||||||||
PREA: Personalized Recommendation Algorithms Toolkit | |||||||||||
文献作者 | Joonseok Lee; Mingxuan Sun; Guy Lebanon | ||||||||||
文献发表年限 | 2012 | ||||||||||
文献关键字 | |||||||||||
tootkit; datasets; explicit feedback; exrecommender systems; collaborative filtering; evaluation metric;库;开源软件 | |||||||||||
摘要描述 | |||||||||||
Recommendation systems are important business applications with significant economic impact. In recent years, a large number of algorithms have been proposed for recommendation systems. In this paper, we describe an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics. In contrast to other packages, our toolkit implements recent state-of-the-art algorithms as well as most classic algorithms. |