HGT4REC: HYPERBOLIC GRAPH TRANSFORMER FOR SEQUENTIAL AND SOCIAL RECOMMENDATION

Authors

  • Rungthip Cobal Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0002-3214-3009
  • Jintana Polsri Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0007-6139-3767
  • Phatthira Keawkerd Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0008-4882-0012
  • Nikorn Kannikaklang Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0003-9966-3524
  • Poorivat Kampeerapaappat Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0001-4981-2195
  • Supasee Duangsai Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0007-8443-367X
  • Suratep Pangerd Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0007-9519-5689
  • Natratanon Kanraweekultana Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0006-0804-291X
  • Wassana Duangmeun Major of Information Technology and Digital Business, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0003-9862-6617
  • Jessadaporn Yanuphrom Major of Finance, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0005-2811-0855
  • Piranun Chantavirod Major of Accounting, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0008-0917-2809
  • Sukanya Kittikhunngam Major of Accounting, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0002-7918-8917
  • Suphattana Tachochalalai Major of Finance, Faculty of Business Administration, Rajamangala University of Technology Krungthep, Bangkok, Thailand https://orcid.org/0009-0007-4617-5382
  • Chinapat Sakunrasrisuay Department of Information Technology and Computer Innovation, Faculty of Management Science and Information Technology, Nakhon Phanom University, Nakhon Phanom, Thailand https://orcid.org/0009-0007-8594-4762

DOI:

https://doi.org/10.55766/sujst11821

Keywords:

Gated Recurrent Unit, Graph Transformer, Hyperbolic Geometry, Sequential Recommendation, Social Recommendation

Abstract

Sequential behaviors and social ties jointly shape user preferences; however, most prior work models them in isolation and relies on shallow fusion in Euclidean space, which struggles to capture temporal drift and hierarchical social structure. We propose a novel framework; HGT4Rec, a Hyperbolic Graph Transformer for Sequential and Social Recommendation. A graph transformer encodes item-transition dependencies to track evolving preferences along the sequence, while a hyperbolic transformer operates on the social graph to represent long-range and hierarchical influence. We further introduce FusionGRU, an adaptive gating module that integrates the two representations into a unified preference state for Top-K prediction. Experiments on Yelp, iFashion, LastFM show that HGT4Rec delivers substantial improvements over the strongest baseline, achieving +348.22% / +314.86%, +383.21% / +210.19%, and +90.85% / +9.83% in Recall@10 and NDCG@10, respectively. Our results demonstrate the value of combining graph transformers with hyperbolic space modeling and the gated fusion for next-item recommendation in sequential and social settings (our code).

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Published

2026-09-03

How to Cite

Cobal, R., Polsri, J., Keawkerd, P., Kannikaklang, N., Kampeerapaappat, P., Duangsai, S., Pangerd, S., Kanraweekultana, N., Duangmeun, W., Yanuphrom, J., Chantavirod, P., Kittikhunngam, S., Tachochalalai, S., & Sakunrasrisuay, C. (2026). HGT4REC: HYPERBOLIC GRAPH TRANSFORMER FOR SEQUENTIAL AND SOCIAL RECOMMENDATION . Suranaree Journal of Science and Technology, 33(4), 030394(1–14). https://doi.org/10.55766/sujst11821

Issue

Vol. 33 No. 4 (2026): July - August

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Research Article

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Copyright (c) 2026 Rungthip Cobal, Jintana Polsri, Phatthira Keawkerd, Nikorn Kannikaklang, Poorivat Kampeerapaappat, Supasee Duangsai, Suratep Pangerd, Natratanon Kanraweekultana, Wassana Duangmeun, Jessadaporn Yanuphrom, Piranun Chantavirod, Sukanya Kittikhunngam, Suphattana Tachochalalai, Chinapat Sakunrasrisuay

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