SiReN: Sign-Aware Recommendation Using Graph Neural Networks

Changwon Seo, Kyeong Joong Jeong, Sungsu Lim, Won Yong Shin

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new Si gn-aware Recommender system based on GNN models. Specifically, SiReN has three key components: 1) constructing a signed bipartite graph for more precisely representing users' preferences, which is split into two edge-disjoint graphs with positive and negative edges each; 2) generating two embeddings for the partitioned graphs with positive and negative edges via a GNN model and a multilayer perceptron (MLP), respectively, and then using an attention model to obtain the final embeddings; and 3) establishing a sign-aware Bayesian personalized ranking (BPR) loss function in the process of optimization. Through comprehensive experiments, we empirically demonstrate that SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.

Original languageEnglish
Pages (from-to)4729-4743
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume35
Issue number4
DOIs
Publication statusPublished - 2024 Apr 1

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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