Skip to content

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

License

Notifications You must be signed in to change notification settings

graytowne/caser

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

15 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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

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)

  • 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

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

About

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

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages