Code for our CIKM 2020 Paper "S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization"
The major contributions of our paper are four self-supervised optimization objectives, which capture item-attribute, sequence-item, sequence-attribute and sequence-subsequence correlations in raw data, respectively. And these optimization objectives are developed in a unified form of mutual information maximization.
Since we conduct extensive experiments on six datasets and under two evaluation scene (ranking with 99 negative items or all items), you can check the ./reproduce/ directory to try your targeted dataset or evaluation scene as you like.
We illustrate the performance of our method comparing with different methods on six datasets. The best performance and the second best performance methods are denoted in bold and underlined fonts respectively.
Considering some recent researchers argue the effectiveness of different ranking strategies for testing recommender systems, we conduct experiments with two mainstream evaluation approaches. It is really time-consuming for the additional experiment, please do not save your star :)
In the PAPER, we pair the ground-truth item with 99 randomly sampled negative items that the user has not interacted with, and report the results of HR@{1, 5, 10}, NDCG@{5, 10} and MRR. The used test files are named as
data-name_sample.txt
The results are shown in the following picture.
We also rank the ground-truth item with all items. We omit the FM and AutoInt because they need enumerate all user-item pairs, which take a very long time. The results are shown in the following picture.
pip install -r requirements.txtdata preprocess
./data/data_process.py
generate negative items for testing
./data/generate_test.py
data-name.txt
one user per line
user_1 item_1 item_2 ...
user_2 item_1 item_2 ...
data-name_sample.txt
one user per line
user_1 neg_item_1 neg_item_2 ...
user_2 neg_item_1 neg_item_2 ...
data-name_item2attributes.json
{item_1:[attr, ...], item_2:[attr, ...], ... }python run_pretrain.py \
--data_name data_nameWe support two evaluation methods. For more details, please check the ./reproduce directory.
- Rank ground-truth item with 99 randomly sampled negative items
python run_finetune_sample.py \
--data_name data_name \
--ckp pretrain_epochs_num- Rank the ground-truth item with all the items
python run_finetune_full.py \
--data_name data_name \
--ckp pretrain_epochs_numIf you find our codes and datasets useful for your research or development, please cite our paper:
@inproceedings{CIKM2020-S3Rec,
author = {Kun Zhou and
Hui Wang and
Wayne Xin Zhao and
Yutao Zhu and
Sirui Wang and
Fuzheng Zhang and
Zhongyuan Wang and
Ji{-}Rong Wen},
title = {S3-Rec: Self-Supervised Learning for Sequential Recommendation with
Mutual Information Maximization},
booktitle = {{CIKM} '20: The 29th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020},
pages = {1893--1902},
publisher = {{ACM}},
year = {2020}
}
If you have any questions for our paper or codes, please send an email to hui.wang@ruc.edu.cn.