Dual prototype attentive graph network for cross-market recommendation

TL;DR

This paper introduces DGRE, a novel graph-based model that captures both shared and market-specific user and item preferences to improve cross-market recommendations, demonstrating enhanced robustness and generalization.

Contribution

The paper proposes DGRE, a dual prototype attentive graph network that models shared and market-specific insights for cross-market recommendation systems.

Findings

DGRE outperforms existing methods on real-world datasets.

Incorporating shared and specific prototypes improves recommendation accuracy.

DGRE enhances model robustness across diverse markets.

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.