  This paper introduces a simple and effective form of data augmentation for
recommender systems. A paraphrase similarity model is applied to widely
available textual data, such as reviews and product descriptions, yielding new
semantic relations that are added to the user-item graph. This increases the
density of the graph without needing further labeled data. The data
augmentation is evaluated on a variety of recommendation algorithms, using
Euclidean, hyperbolic, and complex spaces, and over three categories of Amazon
product reviews with differing characteristics. Results show that the data
augmentation technique provides significant improvements to all types of
models, with the most pronounced gains for knowledge graph-based recommenders,
particularly in cold-start settings, leading to state-of-the-art performance.
