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.

本文收集和发表了一份大数据集，探究了跨市场商品推荐的问题，并提出了 FOREC 模型来完美利用原始数据集和目标市场数据，致力于提高性能并解决市场适应问题，实验显示比竞争基准线效果更优。