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arXiv · 2302.07130

Market-Aware Models for Efficient Cross-Market Recommendation

Samarth Bhargav·Mohammad Aliannejadi·Evangelos Kanoulas
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Abstract

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.

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Samarth Bhargav, Mohammad Aliannejadi, Evangelos Kanoulas. 2023-02-14. Market-Aware Models for Efficient Cross-Market Recommendation. https://doi.org/10.1007/978-3-031-28244-7_9

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