CaserPyTorch
A PyTorch implementation of Convolutional Sequence Embedding Recommendation Model (Caser) from the paper:
Personalized TopN Sequential Recommendation via Convolutional Sequence Embedding, Jiaxi Tang and Ke Wang , WSDM '18
Requirements
 Python 2 or 3
 PyTorch v0.4+
 Numpy
 SciPy
Usage
 Install required packages.
 run
python train_caser.py
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)
L
: length of sequenceT
: number of targetsd
: number of latent dimensionsnv
: number of vertical filtersnh
: number of horizontal filtersac_conv
: activation function for convolution layer (i.e., phi_c in paper)ac_fc
: activation function for fullyconnected 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 TopN 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

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.