Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

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

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

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.

Originalspracheenglisch
TitelFoundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings
Redakteure/-innenDenis Helic, Martin Stettinger, Alexander Felfernig, Gerhard Leitner, Zbigniew W. Ras
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten181-191
Seitenumfang11
ISBN (Print)9783030594909
DOIs
PublikationsstatusVeröffentlicht - 1 Jan 2020
Veranstaltung25th International Symposium on Methodologies for Intelligent Systems - TU Graz, Virtuell, Österreich
Dauer: 23 Sep 202025 Sep 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12117 LNAI
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz25th International Symposium on Methodologies for Intelligent Systems
KurztitelISMIS 2020
LandÖsterreich
OrtVirtuell
Zeitraum23/09/2025/09/20

ASJC Scopus subject areas

  • !!Theoretical Computer Science
  • !!Computer Science(all)

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