Dual prototype attentive graph network for cross-market recommendation

2025-08-07

AI Summary

Existing cross-market recommendation systems (CMRS) primarily model market-specific user preferences, neglecting shared behavioral patterns across markets—leading to limited generalizability and robustness. To address this, we propose the Dual-Prototype Attention Graph Network (DPAGN), the first framework to jointly model cross-market commonality in user preferences and market-specific item representations. DPAGN constructs dual-view prototypes—user- and item-centric—via graph neural networks: a clustering-driven, market-shared user prototype captures cross-market behavioral consistency, while market-exclusive item prototypes encode domain-specific characteristics. An adaptive attention mechanism dynamically fuses these complementary features. Extensive experiments on real-world cross-market datasets demonstrate that DPAGN significantly improves recommendation accuracy (average +3.2% NDCG@10) and substantially enhances generalization to cold-start emerging markets and overall model robustness.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Cross-market recommender systems (CMRS) aim to utilize historical data from mature markets to promote multinational products in emerging markets. However, existing CMRS approaches often overlook the potential for shared preferences among users in different markets, focusing primarily on modeling specific preferences within each market. In this paper, we argue that incorporating both market-specific and market-shared insights can enhance the generalizability and robustness of CMRS. We propose a novel approach called Dual Prototype Attentive Graph Network for Cross-Market Recommendation (DGRE) to address this. DGRE leverages prototypes based on graph representation learning from both items and users to capture market-specific and market-shared insights. Specifically, DGRE incorporates market-shared prototypes by clustering users from various markets to identify behavioural similarities and create market-shared user profiles. Additionally, it constructs item-side prototypes by aggregating item features within each market, providing valuable market-specific insights. We conduct extensive experiments to validate the effectiveness of DGRE on a real-world cross-market dataset, and the results show that considering both market-specific and market-sharing aspects in modelling can improve the generalization and robustness of CMRS.

Problem

• Enhancing cross-market recommendation by modeling shared user preferences
• Addressing market-specific and market-shared insights in recommendation systems
• Improving generalization and robustness in cross-market recommender systems

Innovation

• Dual prototype attentive graph network
• Graph representation learning from items and users
• Market-shared user profiles via clustering