# Cross-domain Recommendation via Deep Domain Adaptation

arXiv: https://arxiv.org/abs/1803.03018 · HTML: https://mlsanity.com/paper/1803.03018/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 12 of 12)

- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [Cross-Market Product Recommendation](https://mlsanity.com/paper/2109.05929/) (2021)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations](https://mlsanity.com/paper/2104.08490/) (2021)
- [An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering](https://mlsanity.com/paper/2112.07191/) (2021)
- [Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems](https://mlsanity.com/paper/2309.01188/) (2023)
- [Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation](https://mlsanity.com/paper/2108.13824/) (2021)
- [Recommending with Recommendations](https://mlsanity.com/paper/2112.00979/) (2021)
- [A cross-domain recommender system using deep coupled autoencoders](https://mlsanity.com/paper/2112.07617/) (2021)
- [Market-Aware Models for Efficient Cross-Market Recommendation](https://mlsanity.com/paper/2302.07130/) (2023)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context](https://mlsanity.com/paper/2509.02890/) (2025)

## Abstract (arXiv)

The behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never used. However, the cross-domain relationships between items and user consumption patterns are not simple, especially when there are few or no common users and items across domains. To address this problem, we propose a content-based cross-domain recommendation method for cold-start users that does not require user- and item- overlap. We formulate recommendation as extreme multi-class classification where labels (items) corresponding to the users are predicted. With this formulation, the problem is reduced to a domain adaptation setting, in which a classifier trained in the source domain is adapted to the target domain. For this, we construct a neural network that combines an architecture for domain adaptation, Domain Separation Network, with a denoising autoencoder for item representation. We assess the performance of our approach in experiments on a pair of data sets collected from movie and news services of Yahoo! JAPAN and show that our approach outperforms several baseline methods including a cross-domain collaborative filtering method.