Collaborative Social Metric Learning in Trust Network for Recommender Systems

Taehan Kim, Wonzoo Chung

Research output: Contribution to journalArticlepeer-review


In this study, a novel top-K ranking recommendation method called collaborative social metric learning (CSML) is proposed, which implements a trust network that provides both user-item and user-user interactions in simple structure. Most existing recommender systems adopting trust networks focus on item ratings, but this does not always guarantee optimal top-K ranking prediction. Conventional direct ranking systems in trust networks are based on sub-optimal correlation approaches that do not consider item-item relations. The proposed CSML algorithm utilizes the metric learning method to directly predict the top-K items in a trust network. A new triplet loss is further proposed, called sociocentric loss, which represents user-user interactions to fully exploit the information contained in a trust network, as an addition to the two commonly used triplet losses in metric learning for recommender systems, which consider user-item and item-item relations. Experimental results demonstrate that the proposed CSML outperformed existing recommender systems for real-world trust network data.

Original languageEnglish
Article number316535
JournalInternational Journal on Semantic Web and Information Systems
Issue number1
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 IGI Global. All rights reserved.


  • Homophily
  • Item Recommendation
  • Metric Learning
  • Recommender Systems
  • Social Recommendation
  • Trust Network
  • User-Item Relation
  • User-User Relation

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications


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