Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

Tomislav Duricic; Hussain Hussain; Emanuel Lacic; Dominik Kowald; Denis Helic; Elisabeth Lex

doi:10.1007/978-3-030-59491-6_17

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings
Editors	Denis Helic, Martin Stettinger, Alexander Felfernig, Gerhard Leitner, Zbigniew W. Ras
Place of Publication	Germany
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	181-191
Number of pages	11
ISBN (Print)	9783030594909
DOIs	https://doi.org/10.1007/978-3-030-59491-6_17
Publication status	Published - 1 Jan 2020
Event	25th International Symposium on Methodologies for Intelligent Systems - online event, Graz, Austria Duration: 23 Sept 2020 → 25 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Science and Business Media Deutschland GmbH

Conference

Conference	25th International Symposium on Methodologies for Intelligent Systems
Abbreviated title	ISMIS 2020
Country/Territory	Austria
City	Graz
Period	23/09/2020 → 25/10/2020

Keywords

Cold-start
Empirical study
Graph embeddings
Recommender systems
Trust networks

Access to Document

10.1007/978-3-030-59491-6_17

Cite this

Duricic, T., Hussain, H., Lacic, E., Kowald, D., Helic, D., & Lex, E. (2020). Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. In D. Helic, M. Stettinger, A. Felfernig, G. Leitner, & Z. W. Ras (Eds.), Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings (pp. 181-191). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59491-6_17

Duricic, Tomislav ; Hussain, Hussain ; Lacic, Emanuel et al. / Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings. editor / Denis Helic ; Martin Stettinger ; Alexander Felfernig ; Gerhard Leitner ; Zbigniew W. Ras. Germany : Springer Science and Business Media Deutschland GmbH, 2020. pp. 181-191 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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note = "25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020 ; Conference date: 23-09-2020 Through 25-09-2020; 25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020 ; Conference date: 23-09-2020 Through 25-10-2020",

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Duricic, T, Hussain, H, Lacic, E, Kowald, D, Helic, D & Lex, E 2020, Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. in D Helic, M Stettinger, A Felfernig, G Leitner & ZW Ras (eds), Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH, Germany, pp. 181-191, 25th International Symposium on Methodologies for Intelligent Systems, Graz, Austria, 23/09/2020. https://doi.org/10.1007/978-3-030-59491-6_17

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. / Duricic, Tomislav; Hussain, Hussain; Lacic, Emanuel et al.
Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings. ed. / Denis Helic; Martin Stettinger; Alexander Felfernig; Gerhard Leitner; Zbigniew W. Ras. Germany: Springer Science and Business Media Deutschland GmbH, 2020. p. 181-191 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

AU - Duricic, Tomislav

AU - Hussain, Hussain

AU - Lacic, Emanuel

AU - Kowald, Dominik

AU - Helic, Denis

AU - Lex, Elisabeth

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PY - 2020/1/1

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N2 - 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.

AB - 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.

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Duricic T, Hussain H, Lacic E, Kowald D, Helic D, Lex E. Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. In Helic D, Stettinger M, Felfernig A, Leitner G, Ras ZW, editors, Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings. Germany: Springer Science and Business Media Deutschland GmbH. 2020. p. 181-191. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59491-6_17

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

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