Enhancing Cross-Market Recommendation System with Graph Isomorphism Networks: A Novel Approach to Personalized User Experience

TL;DR

This paper introduces CrossGR, a novel recommendation model using Graph Isomorphism Networks to enhance personalization and accuracy across diverse global markets, addressing data sparsity and market specificity challenges.

Contribution

The paper presents the CrossGR model that leverages GINs to significantly improve cross-market recommendation performance over existing methods.

Findings

Outperforms benchmarks in NDCG@10 and HR@10 metrics.

Demonstrates robustness across different market segments.

Shows adaptability to evolving market trends.

Abstract

In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with market specificity and data sparsity, especially in new or emerging markets. In this paper, we propose the CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems. It outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments. The CrossGR model is adaptable and accurate, making it well-suited for handling the complexities of cross-market recommendation tasks. Its robustness is demonstrated by consistent performance across different evaluation timeframes, indicating its potential to cater to evolving market trends and user preferences. Our findings suggest that GINs represent a promising direction for CMRs, paving the way for more sophisticated, personalized, and context-aware recommendation systems in the dynamic landscape of global e-commerce.