@asash First, thank you for your great job on the sequential recommendation.
I have noticed that in your article, the results of BERT4Rec on the Beauty dataset cannot be replicated. After investigation, I found that it may be due to inconsistent preprocessing methods on the dataset. I used the Beauty dataset processed by S3Rec (which can replicate the results of SASRec on Beauty) for the experiment. Finally, the experimental results of BERT4Rec can be reproduced.
Inspired by you, I am trying to improve SASRec. As you said in the paper, the main difference between BERT4Rec and SASRec lies in the training objectives or loss function. So I tried to improve the loss function of SASRec. Finally, SASRec using the improved loss function can surpass BERT4Rec on the ML-1M and Beauty datasets, and achieve similar results on the ML-20M and Steam datasets.
I provide code (by fork) to reproduce what I said above. Finally, I hope my findings can be helpful to you.
Thank you again for your great work!
@asash First, thank you for your great job on the sequential recommendation.
I have noticed that in your article, the results of BERT4Rec on the Beauty dataset cannot be replicated. After investigation, I found that it may be due to inconsistent preprocessing methods on the dataset. I used the Beauty dataset processed by S3Rec (which can replicate the results of SASRec on Beauty) for the experiment. Finally, the experimental results of BERT4Rec can be reproduced.
Inspired by you, I am trying to improve SASRec. As you said in the paper, the main difference between BERT4Rec and SASRec lies in the training objectives or loss function. So I tried to improve the loss function of SASRec. Finally, SASRec using the improved loss function can surpass BERT4Rec on the ML-1M and Beauty datasets, and achieve similar results on the ML-20M and Steam datasets.
I provide code (by fork) to reproduce what I said above. Finally, I hope my findings can be helpful to you.
Thank you again for your great work!