LogiRec

This is the implementation of our paper: Towards High-Order Complementary Recommendation via Logical Reasoning Network.

Introduction

In this work, we propose a logical reasoning network: LogiRec to capture the asymmetric complementary relationship between products and seamlessly extend it to the high-order recommendation where more comprehensive and meaningful complementary relationship is learned from a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation.

Dataset

We provide the processed dataset. The data in the default folder is trained for LogiRec_Hybrid model, highOrder is for LogiRec_High, and lowOrder for LogiRec_Low.

Example to run LogiRec

bash example.sh

Citation

@inproceedings{wu2022towards,
title={Towards high-order complementary recommendation via logical reasoning network},
	author={Wu, Longfeng and Zhou, Yao and Zhou, Dawei},
	booktitle={2022 IEEE International Conference on Data Mining (ICDM)},
	pages={1227--1232},
	year={2022},
	organization={IEEE}
}