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.
CITATION STYLE
Duricic, T., Hussain, H., Lacic, E., Kowald, D., Helic, D., & Lex, E. (2020). Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12117 LNAI, pp. 181–191). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59491-6_17
Mendeley helps you to discover research relevant for your work.