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模型名称 TiSASRec: Time Interval Aware Self-Attention for Sequential Recommendation

1. 简介

序列推荐系统寻求利用用户交互的顺序,以便根据用户最近所做的事情来预测他们的下一步行动。传统做法中大多数模型所做的假设是将互动历史视为有序的序列,而不考虑每个互动之间的时间间隔。
文本提出TiSASRec(时间间隔感知自我注意的序列推荐),对序列内的交互的时间戳进行建模,以探索不同时间间隔对下一个项目预测的影响。实验结果表明,该方法在稀疏和稠密数据集以及不同评价指标上均优于各种先进的序列模型。

2. 复现精度

  • 目标精度 (NDCG@10:0.5706,Hit@10:0.8038)
  • 复现 (NDCG@10: 0.5712, HR@10: 0.8055)

3. 数据集

ml-1m

4. 环境依赖

paddlepaddle-gpu=2.2.0

5. 训练评估

nohup python main.py --dataset=ml-1m --train_dir=default --seed 6 > train.log &

部分日志

epoch:60, time: 186.444842(s), valid (NDCG@10: 0.5910, HR@10: 0.8227), test (NDCG@10: 0.5642, HR@10: 0.7985)
epoch:80, time: 248.875761(s), valid (NDCG@10: 0.5929, HR@10: 0.8232), test (NDCG@10: 0.5693, HR@10: 0.8018)
epoch:100, time: 310.499432(s), valid (NDCG@10: 0.5938, HR@10: 0.8210), test (NDCG@10: 0.5738, HR@10: 0.8060)
epoch:120, time: 372.769209(s), valid (NDCG@10: 0.5985, HR@10: 0.8222), test (NDCG@10: 0.5732, HR@10: 0.8015)
epoch:140, time: 434.958108(s), valid (NDCG@10: 0.5990, HR@10: 0.8227), test (NDCG@10: 0.5716, HR@10: 0.8022)

6. TIPC测试

cd PaddleRec
bash test_tipc/prepare.sh ./test_tipc/configs/tisas/train_infer_python.txt 'lite_train_lite_infer'
bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/tisas/train_infer_python.txt 'lite_train_lite_infer'

引用原论文

@inproceedings{li2020time,
  title={Time Interval Aware Self-Attention for Sequential Recommendation},
  author={Li, Jiacheng and Wang, Yujie and McAuley, Julian},
  booktitle={Proceedings of the 13th International Conference on Web Search and Data Mining},
  pages={322--330},
  year={2020}
}

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