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.

翻译：我们考虑跨市场推荐任务，该任务涉及利用来自资源更丰富的辅助源市场数据，对资源匮乏的目标市场进行推荐。现有跨市场推荐工作采用元学习来提升目标市场的推荐性能，但元学习可能复杂且资源密集。本文提出市场感知（MA）模型，该模型直接通过市场嵌入而非跨市场元学习对市场进行建模。这些嵌入将物品表示转换为市场特定的表示。实验表明，MA模型在单目标-源市场的配对场景以及所有市场联合训练的全局模型中均具有显著的有效性和效率。在配对场景中，MA模型在nDCG@10指标上平均在85%的案例中优于市场无关模型，同时训练时间仅为元学习模型的15%。在全局场景中，MA模型在部分市场中始终优于市场无关模型，并在除一个市场外的所有市场中优于基于元学习的方法。我们得出结论，MA模型是元学习的高效且有效的替代方案，尤其在全局场景中表现突出。