ChenGaoDe / PIMI_Rec

Code for the IJCAI-2021 paper: Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation

Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation

Original implementation for paper Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation.

Accepted to IJCAI 2021!

Requirements

• python 3.7
• tensorflow-gpu 1.13
• faiss-gpu 1.6.3
• numpy 1.19.2
• tensorboardX 2.1

Run

Installation

• Install faiss-gpu based on the instructions here: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md

Dataset

• Original links of datasets are:

  • http://jmcauley.ucsd.edu/data/amazon/
  • https://tianchi.aliyun.com/dataset/dataDetail?dataId=649

• You can download the original datasets and preprocess them by yourself. You can run python preprocess/data.py {dataset_name} and python preprocess/category.py {dataset_name} to preprocess the datasets. Note the illegal timestamp.

Training

You can use python main.py --dataset {dataset_name} --time_span {time_threshold} to train a specific model on a dataset. Other hyperparameters can be found in the code. (If you share the server with others or you want to use the specific GPU(s), you may need to set CUDA_VISIBLE_DEVICES.)

For example, you can use python main.py --dataset book --time_span 64 to train PIMI model on Book dataset.

Acknowledgement

The structure of our code is based on ComiRec.