Enhancing cross-market recommendations by addressing negative transfer and leveraging item co-occurrences

• Published in: Information Systems Vol. 124; p. 102388
• Main Authors: Hu, Zheng, Nakagawa, Satoshi, Cai, Shi-Min, Ren, Fuji, Deng, Jiawen
• Format: Journal Article
• Language: English; Japanese
• Published: Elsevier Ltd 01.09.2024; Elsevier BV
• Subjects: Cross-market recommendation; Recommender systems; Transfer learning; Transfer learning; Recommender systems; Cross-market recommendation
• ISSN: 0306-4379; 1873-6076
• DOI: 10.1016/j.is.2024.102388

Real-world multinational e-commerce companies, such as Amazon and eBay, serve in multiple countries and regions. Some markets are data-scarce, while others are data-rich. In recent years, cross-market recommendation (CMR) has been proposed to bolster data-scarce markets by leveraging auxiliary information from data-rich markets. Previous CMR algorithms have employed techniques such as sharing market-agnostic parameters or incorporating inter-market similarity to optimize the performance of CMR. However, the existing approaches have several limitations: (1) They do not fully utilize the valuable information on item co-occurrences obtained from data-rich markets (such as the consistent purchase of mice and keyboards). (2) They ignore the issue of negative transfer stemming from disparities across diverse markets. To address these limitations, we introduce a novel attention-based model that exploits users’ historical behaviors to mine general patterns from item co-occurrences and designs market-specific embeddings to mitigate negative transfer. Specifically, we propose an attention-based user interest mining module to harness the potential of common items as bridges for mining general knowledge from item co-occurrence patterns through rich data derived from global markets. In order to mitigate the adverse effects of negative transfer, we decouple the item representations into market-specific embeddings and market-agnostic embeddings. The market-specific embeddings effectively model the inherent biases associated with different markets, while the market-agnostic embeddings learn generic representations of the items. Extensive experiments conducted on seven real-world datasets illustrate our model’s effectiveness.11Our codes and checkpoints are available at https://github.com/laowangzi/ACMR. Our model outperforms the suboptimal model by an average of 4.82%, 6.82%, 3.87%, and 5.34% across four variants of two metrics. Extensive experiments and analysis demonstrate the effectiveness of our proposed model in mining general item co-occurrence patterns and avoiding negative transfer for data-sparse markets.