Awesome

Caser

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

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

Note: I strongly suggest to use the PyTorch version here, as it has better readability and reproducibility.

Requirements

• Matlab R2015 +
• MatConvNet v1.0

Usage

1. Installing MatConvNet (guide).
2. Change the code to make the path point to your MatConvNet path.
3. Open Matlab and run main_caser.m

Configurations

Data

• Datasets are organized in 2 seperate 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 Reommendation, the rating doesn't matter, so I convert them to all 1.

Model Args (in main_caser.m)

• <code>L</code>: length of sequence
• <code>T</code>: number of targets
• <code>rate_once</code>: whether each item will only be rated once by each user
• <code>early_stop</code>: whether to perform early stop during training
• <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

For easy implementation and flexibility, I didn't implement below things:

• Didn't make mini-batch in parallel.
• Didn't make the model in MatConvNet wrapper.

License

• GNU Lesser General Public License v3.0