@inproceedings{f4cdc2acaecc4ff3ab0113c067ee1cca,
title = "Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering",
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.",
keywords = "Cold-start, Empirical study, Graph embeddings, Recommender systems, Trust networks",
author = "Tomislav Duricic and Hussain Hussain and Emanuel Lacic and Dominik Kowald and Denis Helic and Elisabeth Lex",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-59491-6_17",
language = "English",
isbn = "9783030594909",
series = "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",
pages = "181--191",
editor = "Denis Helic and Martin Stettinger and Alexander Felfernig and Gerhard Leitner and Ras, {Zbigniew W.}",
booktitle = "Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings",
address = "Germany",
note = "25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020 ; Conference date: 23-09-2020 Through 25-09-2020",
}