# Cross-Market Product Recommendation

arXiv: https://arxiv.org/abs/2109.05929 · HTML: https://mlsanity.com/paper/2109.05929/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 57 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [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)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [The Multilingual Amazon Reviews Corpus](https://mlsanity.com/paper/2010.02573/) (2020)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [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)
- [How to Retrain Recommender System? A Sequential Meta-Learning Method](https://mlsanity.com/paper/2005.13258/) (2020)
- [DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns](https://mlsanity.com/paper/1905.10760/) (2019)
- [Towards Question-based Recommender Systems](https://mlsanity.com/paper/2005.14255/) (2020)
- [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)
- [MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection](https://mlsanity.com/paper/2001.10378/) (2020)
- [Cross-domain Recommendation via Deep Domain Adaptation](https://mlsanity.com/paper/1803.03018/) (2018)
- [Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation](https://mlsanity.com/paper/2001.08961/) (2020)
- [Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval](https://mlsanity.com/paper/2109.02789/) (2021)
- [Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping](https://mlsanity.com/paper/1803.01617/) (2018)
- [Learning Personalized Risk Preferences for Recommendation](https://mlsanity.com/paper/2007.02478/) (2020)
- [Personalized Context-Aware Point of Interest Recommendation](https://mlsanity.com/paper/1806.05736/) (2018)
- [A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation](https://mlsanity.com/paper/1909.07131/) (2019)
- [CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users](https://mlsanity.com/paper/2008.10845/) (2020)

## Built on by (most cited first, 22 of 22)

- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://mlsanity.com/paper/2210.12316/) (2022)
- [Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings](https://mlsanity.com/paper/2402.17016/) (2024)
- [Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://mlsanity.com/paper/2305.07633/) (2023)
- [MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding](https://mlsanity.com/paper/2508.11999/) (2025)
- [Benchmark Granularity and Model Robustness for Image-Text Retrieval](https://mlsanity.com/paper/2407.15239/) (2024)
- [New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec Models](https://mlsanity.com/paper/2505.13136/) (2025)
- [Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking](https://mlsanity.com/paper/2302.02636/) (2023)
- [Personalized Federated Domain Adaptation for Item-to-Item Recommendation](https://mlsanity.com/paper/2306.03191/) (2023)
- [AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents](https://mlsanity.com/paper/2510.05598/) (2025)
- [Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation](https://mlsanity.com/paper/2110.12648/) (2021)
- [Pre-trained Recommender Systems: A Causal Debiasing Perspective](https://mlsanity.com/paper/2310.19251/) (2023)
- [Learning Set Functions with Implicit Differentiation](https://mlsanity.com/paper/2412.11239/) (2024)
- [PSCon: Product Search Through Conversations](https://mlsanity.com/paper/2502.13881/) (2025)
- [Market-Aware Models for Efficient Cross-Market Recommendation](https://mlsanity.com/paper/2302.07130/) (2023)
- [Review-Based Hyperbolic Cross-Domain Recommendation](https://mlsanity.com/paper/2403.20298/) (2024)
- [Enhancing Cross-Market Recommendation System with Graph Isomorphism Networks: A Novel Approach to Personalized User Experience](https://mlsanity.com/paper/2409.07850/) (2024)
- [Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering](https://mlsanity.com/paper/2409.16025/) (2024)
- [Transfer Learning for E-commerce Query Product Type Prediction](https://mlsanity.com/paper/2410.07121/) (2024)
- [Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap](https://mlsanity.com/paper/2501.14826/) (2025)
- [Probabilistic Residual Learning for Online Recommendations](https://mlsanity.com/paper/2607.20863/) (2026)
- [Do Multilingual Encoders Produce Language-Consistent Semantic IDs?](https://mlsanity.com/paper/2610.01139/) (2026)

## Abstract (arXiv)

We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypothesize that data from one market can be used to improve performance in another. Only a few studies have been conducted in this area, partly due to the lack of publicly available experimental data. To this end, we collect and release XMarket, a large dataset covering 18 local markets on 16 different product categories, featuring 52.5 million user-item interactions. We introduce and formalize the problem of cross-market product recommendation, i.e., market adaptation. We explore different market-adaptation techniques inspired by state-of-the-art domain-adaptation and meta-learning approaches and propose a novel neural approach for market adaptation, named FOREC. Our model follows a three-step procedure -- pre-training, forking, and fine-tuning -- in order to fully utilize the data from an auxiliary market as well as the target market. We conduct extensive experiments studying the impact of market adaptation on different pairs of markets. Our proposed approach demonstrates robust effectiveness, consistently improving the performance on target markets compared to competitive baselines selected for our analysis. In particular, FOREC improves on average 24% and up to 50% in terms of nDCG@10, compared to the NMF baseline. Our analysis and experiments suggest specific future directions in this research area. We release our data and code for academic purposes.