Towards High-Order Complementary Recommendation via Logical Reasoning Network

TL;DR

This paper introduces LOGIREC, a logical reasoning network that learns product embeddings and transformations to improve high-order complementary recommendations in e-commerce, demonstrating superior performance on real datasets.

Contribution

The paper proposes a novel logical reasoning network, LOGIREC, capable of capturing asymmetric relationships and extending to high-order recommendations for better product suggestions.

Findings

LOGIREC outperforms existing methods on multiple datasets.

Effective in both low-order and high-order recommendation scenarios.

Jointly optimized hybrid network enhances product representation quality.

Abstract

Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.