Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

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Abstract

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
Pages181-191
Number of pages11
ISBN (Print)9783030594909
DOIs
Publication statusPublished - 1 Jan 2020
Event25th International Symposium on Methodologies for Intelligent Systems - TU Graz, Virtuell, Austria
Duration: 23 Sep 202025 Sep 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

Conference

Conference25th International Symposium on Methodologies for Intelligent Systems
Abbreviated titleISMIS 2020
CountryAustria
CityVirtuell
Period23/09/2025/09/20

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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