Trans2D: Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce

This repository provides a reference implementation of Trans2D as described in the paper:

Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce
Uriel Singer, Haggai Roitman, Yotam Eshel, Alexander Nus, Ido Guy, Or Levi, Idan Hasson and Eliyahu Kiperwasser.
International Conference on Web Search and Data Mining, 2022.

The Trans2D algorithm allows to learn complex item-item, attribute-attribute and item-attribute patterns from sequential-data with multiple item attributes.

Requirements

• torch
• pytorch_lightning
• torchmetrics
• transformers

Citation

@inproceedings{wsdm2022-trans2d,
  title     = {Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce},
  author    = {Singer, Uriel and Roitman, Haggai and Eshel, Yotam and Nus, Alexander and Guy, Ido and Levi, Or and Hasson, Idan and Kiperwasser, Eliyahu},
  booktitle = {Proceedings of the 15th ACM International Conference on Web Search and Data Mining},
  year      = {2022},
  month     = {2},
}