The Impact of Cluster Centroid and Text Review Embeddings on Recommendation Methods

Recommendation systems often neglect global patterns that can
be provided by clusters of similar items or even additional infor-
mation such as text. Therefore, we study the impact of integrating
clustering embeddings, review embeddings, and their combinations
with embeddings obtained by a recommender system. Our work
assesses the performance of this approach across various state-of-
the-art recommender system algorithms. Our study highlights the
improvement of recommendation performance through clustering,
particularly evident when combined with review embeddings, and
the enhanced performance of neural methods when incorporating
review embeddings.