  Helpfulness prediction techniques have been widely used to identify and
recommend high-quality online reviews to customers. Currently, the vast
majority of studies assume that a review's helpfulness is self-contained. In
practice, however, customers hardly process reviews independently given the
sequential nature. The perceived helpfulness of a review is likely to be
affected by its sequential neighbors (i.e., context), which has been largely
ignored. This paper proposes a new methodology to capture the missing
interaction between reviews and their neighbors. The first end-to-end neural
architecture is developed for neighbor-aware helpfulness prediction (NAP). For
each review, NAP allows for three types of neighbor selection: its preceding,
following, and surrounding neighbors. Four weighting schemes are designed to
learn context clues from the selected neighbors. A review is then
contextualized into the learned clues for neighbor-aware helpfulness
prediction. NAP is evaluated on six domains of real-world online reviews
against a series of state-of-the-art baselines. Extensive experiments confirm
the effectiveness of NAP and the influence of sequential neighbors on a current
reviews. Further hyperparameter analysis reveals three main findings. (1) On
average, eight neighbors treated with uneven importance are engaged for context
construction. (2) The benefit of neighbor-aware prediction mainly results from
closer neighbors. (3) Equally considering up to five closest neighbors of a
review can usually produce a weaker but tolerable prediction result.
