Cross-Market Product Recommendation

Abstract

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.

Accepted in CIKM 2021

References in corpus (10)

• Unsupervised Domain Adaptation by Backpropagation
• Neural Collaborative Filtering
• CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
• Towards Question-based Recommender Systems
• Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
• DDTCDR: Deep Dual Transfer Cross Domain Recommendation
• CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users
• Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval
• Learning Personalized Risk Preferences for Recommendation
• Selective Transfer Learning for Cross Domain Recommendation