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Diffusion Recommender Model
I do not see any code on how you generated the item embeddings for your datasets. How were the item embeddings created for the Autoencoders? Thanks.
I used the default hyper parameters "!python main.py --cuda --dataset=ml-1m_clean --data_path=../datasets/ml-1m_clean/"
the results are less than 0.1 and the loss is about 180
I am running "sh run.sh amazon-book_clean 5e-4 1e-4 0 0 400 2 [300] [] 0.05 [300] 10 x0 5 0.5 0.001 0.0005 0 1 log 1 0" for L-DiffRec, but It turns out to be with negative beta that out of range. I trace the code and found it uses "linear-var" as noise_schedule. I print the beta result and it is as: [ 5.00000000e-04, -6.25312656e-05, -6.25273557e-05, -6.25234463e-05, -6.25195374e-05]. Could you please help me to check the problem?
您好,感谢您出色的工作和开源的代码,我在复现论文中的实验结果的时候发现lightgcn在ml-1m的效果和论文中的结果差距很大,我想知道论文中的lightgcn的参数是怎么样的,embedding size是多少呢?
ser num:108822iten num:94949data ready.
running k-means on cuda:0..
[running kneans]: 0it [00:00,?it/s,center_shift=0.066783,iteration=1, tol=0
[running kneans]: 1it [00:00,10.89it/s,center_shift=0.002020,iteration=2,tol[running kneans]: 2it [00:00,15.43it/s, center_shift=0.000370,iteration=3, tol[running kneans]: 3it[00:00,23.13it/s,center_shift=0.000370, iteration=3, tol
[running kneansj: 3it [00:00,23.13it/s, center_shift=0.000044,iteration=4,tol[running kneans]: 4it [00:00,25.34it/s, center_shift=0.000044,iteration=4,tol
category length:[9495,85454]
Latent dims of each category:[[30],[270]]Traceback (most recent call last):
File "main.py" , line 133, in
diffusion = gd.GaussianDiffusion(nean_type,args.noise_schedule,
File "/media/wang/study/jhs/DiffRec-main/L-DiffRec/models/gaussian_diffusion
y", line 35, in init
assert (self. betas > 0).all() and (self. betas = 1).all(), "betas out of range"
AssertionError: betas out of range
May I ask the author, when I reproduce L-Diffrec, according to the default parameter execution, there will be this error, I do not understand, please explain.
你好,我尝试使用“ sh run.sh amazon-book_clean 5e-5 0 400 [1000] 10 x0 5 0.0001 0.0005 0.005 0 1 log 1 0 ” 命令运行代码,但是在amazon-book 数据集加载过程中出现 ValueError: cannot reshape array of size 4566535 into shape (2283281,2)。
Line 275 in 57606a6
Thanks for sharing your codes. And I have a question about implementation of eq.4.
For function betas_from_linear_variance in gaussian_diffusion.py, let argument variance be
For eq.4,
For
For
thus
For
thus
However
Hi,
I read in the paper that the sorted interactions are be splited into training, validation, and testing sets with the ratio of 7:1:2. But the valid dataset in this repository is clearly larger than the test dataset, more like 7:2:1. Is there some problem here?
Best.
Notice that the author uses a new linear noise schedule instead of the Linear or cosine schedules used in DDPM. The selection in the code is noise_ schedule='linear var', which corresponds to lines 303-309 in gaussian_diffusion. py, but I do not understand the correspondence between these codes and Eq. 4 in the paper. I hope the author can help me.
Looking forward to your reply very much.
Hi YiyanXu!
Thank you for your insightful work.
Can you share the set of hyperparameters that diffrers from your default values in the script, to reproduce the result of "ML-1M clean dataset"?
https://github.com/YiyanXu/DiffRec/blob/d605bc9178f338f2f16a084367d859d72ff0608d/L-DiffRec/models/gaussian_diffusion.py#L275C9-L275C33
这里函数的意思是从 ts时刻直接减去model预测的noise变成x_start,我不太理解,不应该是变为xts-1的状态吗
Excuse me, after I unrar the amazon-book_clean.rar, I find there missing item_emb.npy. Could you please upload the dataset again?
FileNotFoundError: [Errno 2] No such file or directory: '../datasets/amazon-book_clean/item_emb.npy'
I do not see any code on how you generated the train_list.npy for your datasets. Does this file record all user_id and item_id with interaction records? Or should we only retain data that has been filtered by 5-core?
As title, I am wondering if L-DiffRec is generally better than DiffRec at a rather small scale.
In your paper, you have shown that L-DiffRec is better in the noisy environment. I wonder if you put L-DiffRec in table 2, where will be its ranking among all your compared baselines? Will it generally surpass DiffRec?
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