  In recent years, text-aware collaborative filtering methods have been
proposed to address essential challenges in recommendations such as data
sparsity, cold start problem, and long-tail distribution. However, many of
these text-oriented methods rely heavily on the availability of text
information for every user and item, which obviously does not hold in
real-world scenarios. Furthermore, specially designed network structures for
text processing are highly inefficient for on-line serving and are hard to
integrate into current systems. In this paper, we propose a flexible neural
recommendation framework, named Review Regularized Recommendation, short as R3.
It consists of a neural collaborative filtering part that focuses on prediction
output, and a text processing part that serves as a regularizer. This modular
design incorporates text information as richer data sources in the training
phase while being highly friendly for on-line serving as it needs no on-the-fly
text processing in serving time. Our preliminary results show that by using a
simple text processing approach, it could achieve better prediction performance
than state-of-the-art text-aware methods.
