Comments (15)
The results
NDCG@5, 10, 20
0.2457 0.2475 0.2656
is on ml-lcfn datasets.
To compare with LCFN, you need to use the dataset ml-lcfn, which is the same as the data used in Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters.
from enmf.
Noted with thanks. I will try ml-lcfn.
from enmf.
The results
NDCG@5, 10, 20
0.2457 0.2475 0.2656
is on ml-lcfn datasets.To compare with LCFN, you need to use the dataset ml-lcfn, which is the same as the data used in Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters.
After running on ml-lcfn(with (dropout: 0.5, neg-weight: 0.5),), I got:
499
Updating: time=0.42
loss,loss_no_reg,loss_reg -14270.39457370924 -14270.39457370924 0.0
TopK: [10, 20, 50]
0.17474698281831363 0.24197656885939905
0.26642073600099603 0.2590238286022946
0.42833159703000406 0.30848090465690464
NDCG@10: 0.24197656885939905 < 0.2475
NDCG@20: 0.2590238286022946 < 0.2656
Not the same as reported in README, but is acceptable.
from enmf.
Have you ever read the readme carefully? What is your setting of embedding size? For a fair comparison, we also set the embedding size as 128, which is utilized in the LCFN work.
from enmf.
Have you ever read the readme carefully? What is your setting of embedding size? For a fair comparison, we also set the embedding size as 128, which is utilized in the LCFN work.
Noted! Here is the results:
499
Updating: time=0.73
loss,loss_no_reg,loss_reg -15902.035453464674 -15902.035453464674 0.0
TopK: [10, 20, 50]
0.18050760514556305 0.24640283029068843
0.27622081694706263 0.26572967980513473
0.44025214134898943 0.31696308408475754
NDCG@10: 0.24640283029068843 < 0.2475
NDCG@20: 0.26572967980513473 > 0.2656
Much better!
from enmf.
所以我比较好奇跟selfCF的结果差异如何?可否给我一份你的分隔好训练集和测试集的数据呢?
from enmf.
所以我比较好奇跟selfCF的结果差异如何?可否给我一份你的分隔好训练集和测试集的数据呢?
附件中是 amazon-games的数据5-core处理完的。
x_label列是train/valid/test (0/1/2) 的标记,这个数据分隔是按全局时间序来的(SelfCF一样的)。您那边可以测试一下看看效果。
from enmf.
所以我比较好奇跟selfCF的结果差异如何?可否给我一份你的分隔好训练集和测试集的数据呢?
附件中是 amazon-games的数据5-core处理完的。
x_label列是train/valid/test (0/1/2) 的标记,这个数据分隔是按全局时间序来的(SelfCF一样的)。您那边可以测试一下看看效果。
谢谢,我大概跑了一下,
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout keep_prob')
parser.add_argument('--negative_weight', type=float, default=0.05,
在第100轮时结果如下:
R@20=0.0764
R@50=0.1323
N@20=0.0367
N@50=0.0511
看起来比SelfCF好很多?🤔
R@20=0.0509
R@50=0.0913
N@20=0.0250
N@50=0.0350
from enmf.
嗯,这个结果确实不错。 能分享一下代码不? 谢谢啦! 您那边还是tensorflow吗?~
from enmf.
嗯,这个结果确实不错。 能分享一下代码不? 谢谢啦! 您那边还是tensorflow吗?~
代码就还是github上的代码
from enmf.
所以我比较好奇跟selfCF的结果差异如何?可否给我一份你的分隔好训练集和测试集的数据呢?
附件中是 amazon-games的数据5-core处理完的。
x_label列是train/valid/test (0/1/2) 的标记,这个数据分隔是按全局时间序来的(SelfCF一样的)。您那边可以测试一下看看效果。
games_processed.csv谢谢,我大概跑了一下,
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout keep_prob')
parser.add_argument('--negative_weight', type=float, default=0.05,在第100轮时结果如下:
R@20=0.0764
R@50=0.1323
N@20=0.0367
N@50=0.0511看起来比SelfCF好很多?🤔
R@20=0.0509
R@50=0.0913
N@20=0.0250
N@50=0.0350
嗯,这个结果确实不错。 能分享一下代码不? 谢谢啦! 您那边还是tensorflow吗?~
代码就还是github上的代码
嗯,了解。 您之前的数据是用last one,现在是用global-time line分隔,这个对程序上没有影响吗?
from enmf.
所以我比较好奇跟selfCF的结果差异如何?可否给我一份你的分隔好训练集和测试集的数据呢?
附件中是 amazon-games的数据5-core处理完的。
x_label列是train/valid/test (0/1/2) 的标记,这个数据分隔是按全局时间序来的(SelfCF一样的)。您那边可以测试一下看看效果。
games_processed.csv谢谢,我大概跑了一下,
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout keep_prob')
parser.add_argument('--negative_weight', type=float, default=0.05,
在第100轮时结果如下:
R@20=0.0764
R@50=0.1323
N@20=0.0367
N@50=0.0511
看起来比SelfCF好很多?🤔
R@20=0.0509
R@50=0.0913
N@20=0.0250
N@50=0.0350嗯,这个结果确实不错。 能分享一下代码不? 谢谢啦! 您那边还是tensorflow吗?~
代码就还是github上的代码
嗯,了解。 您之前的数据是用last one,现在是用global-time line分隔,这个对程序上没有影响吗?
没影响,只要把训练集测试集放在目录下,就可以直接跑了,
你的数据我也已经改成我这边可以直接用的格式上传了,https://github.com/chenchongthu/ENMF/tree/master/data/game
from enmf.
所以我比较好奇跟selfCF的结果差异如何?可否给我一份你的分隔好训练集和测试集的数据呢?
附件中是 amazon-games的数据5-core处理完的。
x_label列是train/valid/test (0/1/2) 的标记,这个数据分隔是按全局时间序来的(SelfCF一样的)。您那边可以测试一下看看效果。
games_processed.csv谢谢,我大概跑了一下,
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout keep_prob')
parser.add_argument('--negative_weight', type=float, default=0.05,
在第100轮时结果如下:
R@20=0.0764
R@50=0.1323
N@20=0.0367
N@50=0.0511
看起来比SelfCF好很多?🤔
R@20=0.0509
R@50=0.0913
N@20=0.0250
N@50=0.0350嗯,这个结果确实不错。 能分享一下代码不? 谢谢啦! 您那边还是tensorflow吗?~
代码就还是github上的代码
嗯,了解。 您之前的数据是用last one,现在是用global-time line分隔,这个对程序上没有影响吗?
没影响,只要把训练集测试集放在目录下,就可以直接跑了,
你的数据我也已经改成我这边可以直接用的格式上传了,https://github.com/chenchongthu/ENMF/tree/master/data/game
好的,非常感谢!
from enmf.
所以我比较好奇跟selfCF的结果差异如何?可否给我一份你的分隔好训练集和测试集的数据呢?
附件中是 amazon-games的数据5-core处理完的。
x_label列是train/valid/test (0/1/2) 的标记,这个数据分隔是按全局时间序来的(SelfCF一样的)。您那边可以测试一下看看效果。
games_processed.csv谢谢,我大概跑了一下,
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout keep_prob')
parser.add_argument('--negative_weight', type=float, default=0.05,
在第100轮时结果如下:
R@20=0.0764
R@50=0.1323
N@20=0.0367
N@50=0.0511
看起来比SelfCF好很多?🤔
R@20=0.0509
R@50=0.0913
N@20=0.0250
N@50=0.0350嗯,这个结果确实不错。 能分享一下代码不? 谢谢啦! 您那边还是tensorflow吗?~
代码就还是github上的代码
嗯,了解。 您之前的数据是用last one,现在是用global-time line分隔,这个对程序上没有影响吗?
没影响,只要把训练集测试集放在目录下,就可以直接跑了,
你的数据我也已经改成我这边可以直接用的格式上传了,https://github.com/chenchongthu/ENMF/tree/master/data/game好的,非常感谢!
不客气~随时交流
from enmf.
所以我比较好奇跟selfCF的结果差异如何?可否给我一份你的分隔好训练集和测试集的数据呢?
附件中是 amazon-games的数据5-core处理完的。
x_label列是train/valid/test (0/1/2) 的标记,这个数据分隔是按全局时间序来的(SelfCF一样的)。您那边可以测试一下看看效果。
games_processed.csv谢谢,我大概跑了一下,
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout keep_prob')
parser.add_argument('--negative_weight', type=float, default=0.05,
在第100轮时结果如下:
R@20=0.0764
R@50=0.1323
N@20=0.0367
N@50=0.0511
看起来比SelfCF好很多?🤔
R@20=0.0509
R@50=0.0913
N@20=0.0250
N@50=0.0350嗯,这个结果确实不错。 能分享一下代码不? 谢谢啦! 您那边还是tensorflow吗?~
代码就还是github上的代码
嗯,了解。 您之前的数据是用last one,现在是用global-time line分隔,这个对程序上没有影响吗?
没影响,只要把训练集测试集放在目录下,就可以直接跑了,
你的数据我也已经改成我这边可以直接用的格式上传了,https://github.com/chenchongthu/ENMF/tree/master/data/game好的,非常感谢!
不客气~随时交流
非常感谢耐心解答。 我刚检查了一下,发现我数据给你错了,上面是一个小样本的测试,全部的在下面:
对了,您有微信不(我的303432874)? 能加一下方便联系不? 谢谢啦!
games5_processed.csv
from enmf.
Related Issues (8)
- After training by the default args, the result isn't good HOT 5
- DHCF实现细节讨论 HOT 4
- Can not reproduce the results of ENMF on ml-lcfn dataset as claimed in README HOT 2
- Recall指标对齐 HOT 2
- ml-1m数据集的validation data HOT 8
- The nDCG formula may not consistent between your paper and implementation? HOT 2
- 想请问下您有没有考虑实现过非负采样+lightGCN这样的模型呢?实验效果会比MF为基础模型好吗? HOT 2
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from enmf.