zyang1580/sml

This is an implementation for our SIGIR 2020 paper: How to Retrain Recommender System? A Sequential Meta-Learning Method.

SML

This is an implementation for our SIGIR 2020 paper: How to Retrain Recommender System? A Sequential Meta-Learning Method.

Contributors: Yang Zhang, Chenxu Wang, Fuli Feng, Xiangnan He

Requirements

pytorch >= 1.2

numpy

Parameters

• --MF_lr: learning rate for $\hat(w)_t$
• --TR_lr: learning rate for Transfer
• --l2： $\lambda_1$ in paper
• --TR_l2: $\lambda_2$ in paper
• --MF_epochs: epochs of learning MF $\hat(w)_t$ (line 6 in Alg 1 in paper)
• --TR_epochs: epochs of learning Transfer $\theta$ (line 9 in Alg 1 in paper)
• --multi_num: stop condition (line 4 in Alg 1 in paper)
• others: read help, or "python main_yelp.py --help"

Dataset

Save as array,

• train: period_num.npy (user_id,item_id)
• test: period_num.npy (user_id,pos_item_id, neg_item_id)
• Dataset URL: https://rec.ustc.edu.cn/share/1e40a9b0-c80a-11ea-b178-77f793cf5b55 (This is Yelp, Adressa will be uploaded later)

Examples

Yelp

nohup python main_yelp.py --MF_epochs=1 --TR_epochs=1 --multi_num=10 > yelp_log.out &

or

python main_yelp.py --MF_epochs=1 --TR_epochs=1 --multi_num=10

Adressa

nohup python main_news.py --MF_epochs=2 --TR_epochs=2 --multi_num=7 > yelp_log.out &

for Adressa, if we set the MF_epochs and TR_epochs same to paper (=1) , we can also get a similar performance if we adjust multi_num.