  Review-based recommender systems have gained noticeable ground in recent
years. In addition to the rating scores, those systems are enriched with
textual evaluations of items by the users. Neural language processing models,
on the other hand, have already found application in recommender systems,
mainly as a means of encoding user preference data, with the actual textual
description of items serving only as side information. In this paper, a novel
approach to incorporating the aforementioned models into the recommendation
process is presented. Initially, a neural language processing model and more
specifically the paragraph vector model is used to encode textual user reviews
of variable length into feature vectors of fixed length. Subsequently this
information is fused along with the rating scores in a probabilistic matrix
factorization algorithm, based on maximum a-posteriori estimation. The
resulting system, ParVecMF, is compared to a ratings' matrix factorization
approach on a reference dataset. The obtained preliminary results on a set of
two metrics are encouraging and may stimulate further research in this area.
