  User reviews contain rich semantics towards the preference of users to
features of items. Recently, many deep learning based solutions have been
proposed by exploiting reviews for recommendation. The attention mechanism is
mainly adopted in these works to identify words or aspects that are important
for rating prediction. However, it is still hard to understand whether a user
likes or dislikes an aspect of an item according to what viewpoint the user
holds and to what extent, without examining the review details. Here, we
consider a pair of a viewpoint held by a user and an aspect of an item as a
logic unit. Reasoning a rating behavior by discovering the informative logic
units from the reviews and resolving their corresponding sentiments could
enable a better rating prediction with explanation. To this end, in this paper,
we propose a capsule network based model for rating prediction with user
reviews, named CARP. For each user-item pair, CARP is devised to extract the
informative logic units from the reviews and infer their corresponding
sentiments. The model firstly extracts the viewpoints and aspects from the user
and item review documents respectively. Then we derive the representation of
each logic unit based on its constituent viewpoint and aspect. A sentiment
capsule architecture with a novel Routing by Bi-Agreement mechanism is proposed
to identify the informative logic unit and the sentiment based representations
in user-item level for rating prediction. Extensive experiments are conducted
over seven real-world datasets with diverse characteristics. Our results
demonstrate that the proposed CARP obtains substantial performance gain over
recently proposed state-of-the-art models in terms of prediction accuracy.
Further analysis shows that our model can successfully discover the
interpretable reasons at a finer level of granularity.
