Cross-Market Recommendation with Two-Stage Graph Learning

Emre Kose, Yusuf Yaslan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Cross-market suggestion is a technique used by social media, e-commerce platforms, and other online platforms to suggest to users' goods or services from several markets or domains. However, user engagement data (clicks, sales, and reviews) with products reveals various biases specific to certain markets, making recommendations more difficult. On the other hand, the lack of data in other markets can make it challenging to train models. Recently, the FOREC model that applies market adaptation has shown good performance on cross market recommendation problem. In this paper we propose a combined framework that employs the Light Graph Convolution Network (LGCN) algorithm, which has both market-agnostic and market-specific models in learning cycle like FOREC but has a less complex architecture than it. The experimental results reveal that our two-stage strategy outperforms FOREC's all findings with improvements ranging from 5 to 8 percentage points with the help of an enhanced 1 to 2 percent of the market-agnostic phase in terms of nDCG@10 evaluation.

Original languageEnglish
Title of host publicationUBMK 2023 - Proceedings
Subtitle of host publication8th International Conference on Computer Science and Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9798350340815
Publication statusPublished - 2023
Event8th International Conference on Computer Science and Engineering, UBMK 2023 - Burdur, Turkey
Duration: 13 Sept 202315 Sept 2023

Publication series

NameUBMK 2023 - Proceedings: 8th International Conference on Computer Science and Engineering


Conference8th International Conference on Computer Science and Engineering, UBMK 2023

Bibliographical note

Publisher Copyright:
© 2023 IEEE.


  • cross-market recommendation
  • hybrid recommender systems
  • market-adaptation


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