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Caser-PyTorch

A PyTorch implementation of Convolutional Sequence Embedding Recommendation Model (Caser) from the paper:

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding, Jiaxi Tang and Ke Wang , WSDM '18

Requirements

• Python 2 or 3
• PyTorch v0.4+
• Numpy
• SciPy

Usage

1. Install required packages.
2. run <code>python train_caser.py</code>

Configurations

Data

• Datasets are organized into 2 separate files: train.txt and test.txt

• Same to other data format for recommendation, each file contains a collection of triplets:

  user item rating

  The only difference is the triplets are organized in time order.

• As the problem is Sequential Recommendation, the rating doesn't matter, so I convert them to all 1.

Model Args (in train_caser.py)

• <code>L</code>: length of sequence
• <code>T</code>: number of targets
• <code>d</code>: number of latent dimensions
• <code>nv</code>: number of vertical filters
• <code>nh</code>: number of horizontal filters
• <code>ac_conv</code>: activation function for convolution layer (i.e., phi_c in paper)
• <code>ac_fc</code>: activation function for fully-connected layer (i.e., phi_a in paper)
• <code>drop_rate</code>: drop ratio when performing dropout

Citation

If you use this Caser in your paper, please cite the paper:

@inproceedings{tang2018caser,
  title={Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding},
  author={Tang, Jiaxi and Wang, Ke},
  booktitle={ACM International Conference on Web Search and Data Mining},
  year={2018}
}

Comments

1. This PyTorch version may get better performance than what the paper reports.

   When d=50, L=5, T=3, and set other arguments to default, after 20 epochs, mAP may get to 0.17 on the test set.

Acknowledgment

This project (utils.py, interactions.py, etc.) is heavily built on Spotlight. Thanks Maciej Kula for his great work.