  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.
