From bd16e8087dc1c3e1cd39980f2d4bb9868702f6ef Mon Sep 17 00:00:00 2001 From: Haw-Shiuan Chang Date: Sat, 2 Mar 2024 14:43:21 -0800 Subject: [PATCH] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 7c8c93dd..5bfea01f 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,10 @@

+## News + +You can use our models (gru4reccpr and sasreccpr) in the latest version of RecBole. To adjust the hyperparameters, please check the comments in the following config files: [GRU4RecCPR](https://github.com/RUCAIBox/RecBole/blob/master/recbole/properties/model/GRU4RecCPR.yaml) and [SASRecCPR](https://github.com/RUCAIBox/RecBole/blob/master/recbole/properties/model/SASRecCPR.yaml). + ## Introduction The similarity structure of the global item embedding in the softmax layer sometimes forces the single hidden state embedding to be close to new items when copying is a better choice, while sometimes forcing the hidden state to be close to the items from the input inappropriately. To alleviate the problem, we adapt the recently-proposed softmax alternatives such as softmax-CPR code from https://github.com/iesl/Softmax-CPR to sequential recommendation tasks and demonstrate that the new softmax architectures unleash the capability of the neural encoder on learning when to copy and when to exclude the items from the input sequence. By only making some simple modifications on the output softmax layer for SASRec and GRU4Rec, softmax-CPR achieves consistent improvement in 12 datasets. With almost the same model size, our best method not only improves the average NDCG@10 of GRU4Rec in 5 datasets with duplicated items by 10% (4%-17% individually) but also improves 7 datasets without duplicated items by 24% (8%-39%)!