# A cross-domain recommender system using deep coupled autoencoders

arXiv: https://arxiv.org/abs/2112.07617 · HTML: https://mlsanity.com/paper/2112.07617/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [CoNet: Collaborative Cross Networks for Cross-Domain Recommendation](https://mlsanity.com/paper/1804.06769/) (2018)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [DDTCDR: Deep Dual Transfer Cross Domain Recommendation](https://mlsanity.com/paper/1910.05189/) (2019)
- [CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network](https://mlsanity.com/paper/2005.10549/) (2020)
- [DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns](https://mlsanity.com/paper/1905.10760/) (2019)
- [A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions](https://mlsanity.com/paper/2108.03357/) (2021)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems](https://mlsanity.com/paper/1903.10794/) (2019)
- [Cross-domain Recommendation via Deep Domain Adaptation](https://mlsanity.com/paper/1803.03018/) (2018)
- [Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations](https://mlsanity.com/paper/2104.08490/) (2021)
- [Training Deep AutoEncoders for Collaborative Filtering](https://mlsanity.com/paper/1708.01715/) (2017)

## Abstract (arXiv)

Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items without sharing sensitive information regarding the users' behavior, and thus avoiding the leak of user privacy. In light of this scenario, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations, thus transferring beneficial information from the source to the target domain. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learnt to map the item-latent factors across domains. Extensive numerical experiments on two publicly available benchmark datasets are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks.