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.

• Matlab R2015 +
• MatConvNet v1.0

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

• 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.

• L: length of sequence
• T: number of targets
• rate_once: whether each item will only be rated once by each user
• early_stop: whether to perform early stop during training
• d: number of latent dimensions
• nv: number of vertical filters
• nh: number of horizontal filters
• ac_conv: activation function for convolution layer (i.e., phi_c in paper)
• ac_fc: activation function for fully-connected layer (i.e., phi_a in paper)
• drop_rate: drop ratio when performing dropout

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}
}

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.

• GNU Lesser General Public License v3.0