# Knowledge-aware Complementary Product Representation Learning

## TL;DR

This paper introduces a knowledge-aware dual embedding approach for learning product representations that effectively capture asymmetric and non-transitive complementary relationships from noisy purchase data, improving recommendation accuracy.

## Contribution

It proposes a novel dual embedding framework with multi-task learning and Bayesian network modeling to better capture the complex properties of product complementariness.

## Key findings

- Outperforms state-of-the-art methods in classification and recommendation tasks.
- Effectively handles noisy, sparse purchase data.
- Scales efficiently to large datasets with millions of items and users.

## Abstract

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers.