# Semi-supervised Adversarial Learning for Complementary Item Recommendation

arXiv: https://arxiv.org/abs/2303.05812 · HTML: https://mlsanity.com/paper/2303.05812/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 53 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Inferring Networks of Substitutable and Complementary Products](https://mlsanity.com/paper/1506.08839/) (2015)
- [Evaluation of Session-based Recommendation Algorithms](https://mlsanity.com/paper/1803.09587/) (2018)
- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [Product Knowledge Graph Embedding for E-commerce](https://mlsanity.com/paper/1911.12481/) (2019)
- [Measuring the Business Value of Recommender Systems](https://mlsanity.com/paper/1908.08328/) (2019)
- [Neural Compatibility Modeling with Attentive Knowledge Distillation](https://mlsanity.com/paper/1805.00313/) (2018)
- [Personalized Embedding-based e-Commerce Recommendations at eBay](https://mlsanity.com/paper/2102.06156/) (2021)
- [A Black-Box Attack Model for Visually-Aware Recommender Systems](https://mlsanity.com/paper/2011.02701/) (2020)
- [Learning the Compositional Visual Coherence for Complementary Recommendations](https://mlsanity.com/paper/2006.04380/) (2020)
- [Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations](https://mlsanity.com/paper/1807.03521/) (2018)
- [Inferring Complementary Products from Baskets and Browsing Sessions](https://mlsanity.com/paper/1809.09621/) (2018)
- [Session-based Complementary Fashion Recommendations](https://mlsanity.com/paper/1908.08327/) (2019)
- [Deep Style Match for Complementary Recommendation](https://mlsanity.com/paper/1708.07938/) (2017)
- [Deep Item-based Collaborative Filtering for Sparse Implicit Feedback](https://mlsanity.com/paper/1812.10546/) (2018)
- [c^+GAN: Complementary Fashion Item Recommendation](https://mlsanity.com/paper/1906.05596/) (2019)
- [Complementary-Similarity Learning using Quadruplet Network](https://mlsanity.com/paper/1908.09928/) (2019)
- [Collaborative Image Understanding](https://mlsanity.com/paper/2210.11907/) (2022)

## Built on by (most cited first, 5 of 5)

- [Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com](https://mlsanity.com/paper/2508.03702/) (2025)
- [Complementary Recommendation in E-commerce: Definition, Approaches, and Future Directions](https://mlsanity.com/paper/2403.16135/) (2024)
- [LLM-Enhanced Reranking for Complementary Product Recommendation](https://mlsanity.com/paper/2507.16237/) (2025)
- [Grounding Free-Form Instructions for Fashion Complementary Image Generation](https://mlsanity.com/paper/2608.23302/) (2026)
- [Beyond Co-purchase Relation: Evolution of Complementary Recommendations at Allegro](https://mlsanity.com/paper/2609.05063/) (2026)

## Abstract (arXiv)

Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.