# Market-Aware Models for Efficient Cross-Market Recommendation

arXiv: https://arxiv.org/abs/2302.07130 · HTML: https://mlsanity.com/paper/2302.07130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [CoNet: Collaborative Cross Networks for Cross-Domain Recommendation](https://mlsanity.com/paper/1804.06769/) (2018)
- [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)
- [Cross-Market Product Recommendation](https://mlsanity.com/paper/2109.05929/) (2021)
- [Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation](https://mlsanity.com/paper/2005.10473/) (2020)
- [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)
- [Learning Personalized Risk Preferences for Recommendation](https://mlsanity.com/paper/2007.02478/) (2020)
- [CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users](https://mlsanity.com/paper/2008.10845/) (2020)

## Abstract (arXiv)

We consider the cross-market recommendation (CMR) task, which involves recommendation in a low-resource target market using data from a richer, auxiliary source market. Prior work in CMR utilised meta-learning to improve recommendation performance in target markets; meta-learning however can be complex and resource intensive. In this paper, we propose market-aware (MA) models, which directly model a market via market embeddings instead of meta-learning across markets. These embeddings transform item representations into market-specific representations. Our experiments highlight the effectiveness and efficiency of MA models both in a pairwise setting with a single target-source market, as well as a global model trained on all markets in unison. In the former pairwise setting, MA models on average outperform market-unaware models in 85% of cases on nDCG@10, while being time-efficient - compared to meta-learning models, MA models require only 15% of the training time. In the global setting, MA models outperform market-unaware models consistently for some markets, while outperforming meta-learning-based methods for all but one market. We conclude that MA models are an efficient and effective alternative to meta-learning, especially in the global setting.