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TLSAN

This is the implementation for our published paper: TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation. The full-text is also available here: https://www.researchgate.net/publication/349912702_TLSAN_Time-aware_Long-_and_Short-term_Attention_Network_for_Next-item_Recommendation. Here are the brief introductions to the dataset and the experiment results.

Environments

  • Python=3.5
  • Tensorflow=1.8.0
  • Numpy=1.14.2
  • Pandas=0.24.1

Datasets

Amazon exposes the official datasets (http://jmcauley.ucsd.edu/data/amazon/) which have filtered out users and items with less than 5 reviews and removed a large amount of invalid data. Because of above advantages, these datasets are widely utilized by researchers. We also chose Amazon's dataset for experiments. In our experiments, only users, items, interactions, and category information are utilized. We do the preprocessing in the following two steps:

  1. Remove the users whose interactions less than 10 and the items which interactions less than 8 to ensure the effectiveness of each user and item.
  2. Select the users with more than 4 sessions, and select up to 90 behavior records for the remaining users. This step guarantees the existence of long- and short-term behavior records and all behavior records occurred within recent three months.

Statistics (after preprocessing)

Datasets users items categories samples avg.
items/cate		 avg.
behaviors/item		 avg.
behaviors/user
Electronics 39991 22048 673 561100 32.8 25.4 14.0
CDs-Vinyl 24179 27602 310 470087 89.0 17.0 19.4
Clothing-Shoes 2010 1723 226 13157 7.6 7.6 6.5
Digital-Music 1659 1583 53 28852 29.9 18.2 17.4
Office-Products 1720 901 170 29387 5.3 32.6 17.0
Movies-TV 35896 28589 15 752676 1905.9 20.9 26.3
Beauty 3783 2658 179 54225 14.8 20.4 14.3
Home-Kitchen 11567 7722 683 143088 11.3 12.3 18.5
Video-Games 5436 4295 58 83748 74.1 19.5 15.4
Toys-and-Games 2677 2474 221 37515 11.2 15.2 14.0

Experiment results

Datasets ATRank BPR-MF CNN CSAN– LSPM PACA Bi-LSTM SHAN TLSAN
Electronics 0.8659 0.7457 0.8450 0.8005 0.7333 0.8322 0.8495 0.7542 0.9230
CDs-Vinyl 0.8999 0.7684 0.8438 0.7943 0.8594 0.8919 0.8969 0.7138 0.9651
Clothing-Shoes 0.6761 0.6283 0.6712 0.5866 0.6443 0.5313 0.7004 0.7284 0.9363
Digital-Music 0.8601 0.7896 0.8131 0.7685 0.8270 0.9638 0.8468 0.7794 0.9753
Office-Products 0.9162 0.5610 0.8930 0.8401 0.7889 0.8994 0.8628 0.9576 0.9773
Movies-TV 0.8662 0.7654 0.7479 0.7958 0.8003 0.8055 0.8743 0.7771 0.8986
Beauty 0.8160 0.6846 0.7639 0.7620 0.7748 0.9016 0.8231 0.8953 0.9368
Home-Kitchen 0.7039 0.6352 0.7075 0.6820 0.6672 0.8165 0.7373 0.8230 0.8950
Video-Games 0.8809 0.6609 0.8598 0.8033 0.8449 0.8763 0.8598 0.9216 0.9459
Toys-Games 0.8139 0.6294 0.7788 0.7157 0.7708 0.8495 0.8012 0.8797 0.9309

How to run the codes

Download raw data and preprocess it with utils:

sh 0_download_raw.sh
python3 1_convert_pd.py
python3 2_remap_id.py

Build dataset for each model:

python3 build_dataset.py

Train and evaluate the model:

python3 train.py