Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

Tomislav Duricic*, Hussain Hussain, Emanuel Lacic, Dominik Kowald, Denis Helic, Elisabeth Lex

*Corresponding author for this work

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


In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approaches from four method families: (i) factorization-based, (ii) random walk-based, (iii) deep learning-based, and (iv) the Large-scale Information Network Embedding (LINE) approach. We find that across the four families, random-walk-based approaches consistently achieve the best accuracy. Besides, they result in highly novel and diverse recommendations. Furthermore, our results show that the use of graph embeddings in trust-based collaborative filtering significantly improves user coverage.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings
EditorsDenis Helic, Martin Stettinger, Alexander Felfernig, Gerhard Leitner, Zbigniew W. Ras
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030594909
Publication statusPublished - 1 Jan 2020
Event25th International Symposium on Methodologies for Intelligent Systems - TU Graz, Virtuell, Austria
Duration: 23 Sept 202025 Sept 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12117 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Symposium on Methodologies for Intelligent Systems
Abbreviated titleISMIS 2020


  • Cold-start
  • Empirical study
  • Graph embeddings
  • Recommender systems
  • Trust networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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