  Many recent state-of-the-art recommender systems such as D-ATT, TransNet and
DeepCoNN exploit reviews for representation learning. This paper proposes a new
neural architecture for recommendation with reviews. Our model operates on a
multi-hierarchical paradigm and is based on the intuition that not all reviews
are created equal, i.e., only a select few are important. The importance,
however, should be dynamically inferred depending on the current target. To
this end, we propose a review-by-review pointer-based learning scheme that
extracts important reviews, subsequently matching them in a word-by-word
fashion. This enables not only the most informative reviews to be utilized for
prediction but also a deeper word-level interaction. Our pointer-based method
operates with a novel gumbel-softmax based pointer mechanism that enables the
incorporation of discrete vectors within differentiable neural architectures.
Our pointer mechanism is co-attentive in nature, learning pointers which are
co-dependent on user-item relationships. Finally, we propose a multi-pointer
learning scheme that learns to combine multiple views of interactions between
user and item. Overall, we demonstrate the effectiveness of our proposed model
via extensive experiments on \textbf{24} benchmark datasets from Amazon and
Yelp. Empirical results show that our approach significantly outperforms
existing state-of-the-art, with up to 19% and 71% relative improvement when
compared to TransNet and DeepCoNN respectively. We study the behavior of our
multi-pointer learning mechanism, shedding light on evidence aggregation
patterns in review-based recommender systems.
