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.

本文提出了一种基于市场感知的模型（MA models）用于跨市场推荐，在单个目标市场上的实验表明，与无市场感知的模型相比，MA models 的效果更好，且训练时间更加高效，其在全局范围内也表现优秀。