# Knowledge-aware Complementary Product Representation Learning

arXiv: https://arxiv.org/abs/1904.12574 · HTML: https://mlsanity.com/paper/1904.12574/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2019.

## Built on by (most cited first, 10 of 10)

- [Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://mlsanity.com/paper/2305.07633/) (2023)
- [Towards High-Order Complementary Recommendation via Logical Reasoning Network](https://mlsanity.com/paper/2212.04966/) (2022)
- [Theoretical Understandings of Product Embedding for E-commerce Machine Learning](https://mlsanity.com/paper/2102.12029/) (2021)
- [Causal Structure Learning with Recommendation System](https://mlsanity.com/paper/2210.10256/) (2022)
- [Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders](https://mlsanity.com/paper/2211.09072/) (2022)
- [Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?](https://mlsanity.com/paper/2304.04330/) (2023)
- [Within-basket Recommendation via Neural Pattern Associator](https://mlsanity.com/paper/2401.16433/) (2024)
- [Complementary Recommendation in E-commerce: Definition, Approaches, and Future Directions](https://mlsanity.com/paper/2403.16135/) (2024)
- [Variational Inference for Category Recommendation in E-Commerce platforms](https://mlsanity.com/paper/2104.07748/) (2021)
- [Beyond Co-purchase Relation: Evolution of Complementary Recommendations at Allegro](https://mlsanity.com/paper/2609.05063/) (2026)

## Abstract (arXiv)

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.