This repository contains the implementation of 'Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching', accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019).
- Download the dataset containing market basket trajectories.
- Create product embeddings using the product2vec.py script.
- Run main.py to compute the distances and predictions of market basket trajectories similar to the ones under investigation
Please consider citing us if you find this helpful for your work:
@inproceedings{Kraus:2019:PPP:3292500.3330791,
author = {Kraus, Mathias and Feuerriegel, Stefan},
title = {Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
series = {KDD '19},
year = {2019},
doi = {10.1145/3292500.3330791},
}