  This paper explores a novel application of textual semantic similarity to
user-preference representation for rating prediction. The approach represents a
user's preferences as a graph of textual snippets from review text, where the
edges are defined by semantic similarity. This textual, memory-based approach
to rating prediction enables review-based explanations for recommendations. The
method is evaluated quantitatively, highlighting that leveraging text in this
way outperforms both strong memory-based and model-based collaborative
filtering baselines.
