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

Sümeyye Öztürk, Ahmed Burak Ercan, Resul Tugay, Şule Gündüz Öğüdücü

TL;DR

The CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems, outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments.

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.

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

TL;DR

The CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems, outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments.

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.
Paper Structure (16 sections, 5 equations, 1 figure, 3 tables)

This paper contains 16 sections, 5 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. METHODOLOGY
  4. Data Representation
  5. Notation
  6. Graph Isomorphism Network Architecture
  7. Model Optimization and Hyperparameters
  8. EXPERIMENTS
  9. Data & Data Analysis
  10. Dataset Structure:
  11. Data Characteristics
  12. Evaluation Metrics
  13. Baselines
  14. Hyperparameter Configuration
  15. Results and Discussion
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Overview of the CrossGR framework for enhancing cross-market recommendations. Input of the model is a bipartite graph that represents interactions between users and items. The model utilizes two GINConv layers to transform these interactions into an embedding space. Then concationation of these embbeddings is passed through an MLP layer to predict the interaction scores. Lastly, a sigmoid function is applied to convert the scores into probabilities, providing the final recommendation prediction.