# RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems

arXiv: https://arxiv.org/abs/1903.10794 · HTML: https://mlsanity.com/paper/1903.10794/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 13 of 13)

- [Cross-Market Product Recommendation](https://mlsanity.com/paper/2109.05929/) (2021)
- [Data Scarcity in Recommendation Systems: A Survey](https://mlsanity.com/paper/2312.10073/) (2023)
- [RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation](https://mlsanity.com/paper/2111.10093/) (2021)
- [Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles](https://mlsanity.com/paper/2206.11433/) (2022)
- [Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation](https://mlsanity.com/paper/2202.04920/) (2022)
- [Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2206.08050/) (2022)
- [G3SR: Global Graph Guided Session-based Recommendation](https://mlsanity.com/paper/2203.06467/) (2022)
- [Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels](https://mlsanity.com/paper/2102.06950/) (2021)
- [A cross-domain recommender system using deep coupled autoencoders](https://mlsanity.com/paper/2112.07617/) (2021)
- [Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach](https://mlsanity.com/paper/2204.11602/) (2022)
- [Market-Aware Models for Efficient Cross-Market Recommendation](https://mlsanity.com/paper/2302.07130/) (2023)
- [Transferable Sequential Recommendation via Vector Quantized Meta Learning](https://mlsanity.com/paper/2411.01785/) (2024)
- [Modeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping Settings](https://mlsanity.com/paper/2508.16210/) (2025)

## Abstract (arXiv)

Data sparsity and data imbalance are practical and challenging issues in cross-domain recommender systems. This paper addresses those problems by leveraging the concepts which derive from representation learning, adversarial learning and transfer learning (particularly, domain adaptation). Although various transfer learning methods have shown promising performance in this context, our proposed novel method RecSys-DAN focuses on alleviating the cross-domain and within-domain data sparsity and data imbalance and learns transferable latent representations for users, items and their interactions. Different from existing approaches, the proposed method transfers the latent representations from a source domain to a target domain in an adversarial way. The mapping functions in the target domain are learned by playing a min-max game with an adversarial loss, aiming to generate domain indistinguishable representations for a discriminator. Four neural architectural instances of ResSys-DAN are proposed and explored. Empirical results on real-world Amazon data show that, even without using labeled data (i.e., ratings) in the target domain, RecSys-DAN achieves competitive performance as compared to the state-of-the-art supervised methods. More importantly, RecSys-DAN is highly flexible to both unimodal and multimodal scenarios, and thus it is more robust to the cold-start recommendation which is difficult for previous methods.