  Nowadays, neural network (NN) and deep learning (DL) techniques are widely
adopted in many applications, including recommender systems. Given the sparse
and stochastic nature of collaborative filtering (CF) data, recent works have
critically analyzed the effective improvement of neural-based approaches
compared to simpler and often transparent algorithms for recommendation.
Previous results showed that NN and DL models can be outperformed by
traditional algorithms in many tasks. Moreover, given the largely black-box
nature of neural-based methods, interpretable results are not naturally
obtained. Following on this debate, we first present a transparent
probabilistic model that topologically organizes user and product latent
classes based on the review information. In contrast to popular neural
techniques for representation learning, we readily obtain a statistical,
visualization-friendly tool that can be easily inspected to understand user and
product characteristics from a textual-based perspective. Then, given the
limitations of common embedding techniques, we investigate the possibility of
using the estimated interpretable quantities as model input for a rating
prediction task. To contribute to the recent debates, we evaluate our results
in terms of both capacity for interpretability and predictive performances in
comparison with popular text-based neural approaches. The results demonstrate
that the proposed latent class representations can yield competitive predictive
performances, compared to popular, but difficult-to-interpret approaches.
