Helpfulness prediction techniques have been widely incorporated into online decision support systems to identify high-quality reviews. Most current studies on helpfulness prediction assume that a review's helpfulness only relies on information from itself. In practice, however, consumers hardly process reviews independently because reviews are displayed in sequence; a review is more likely to be affected by its adjacent neighbors in the sequence, which is largely understudied. In this paper, we proposed the first end-to-end neural architecture to capture the missing interaction between reviews and their neighbors. Our model allows for a total of 12 (three selection × four aggregation) schemes that contextualize a review into the context clues learned from its neighbors. We evaluated our model on six domains of real-world online reviews against a series of state-of-the-art baselines. Experimental results confirm the influence of sequential neighbors on reviews and show that our model significantly outperforms the baselines by 1% to 5%. We further revealed how reviews are influenced by their neighbors during helpfulness perception via extensive analysis. The results and findings of our work provide theoretical contributions to the field of review helpfulness prediction and offer insights into practical decision support system design.

有用性预测技术已被广泛纳入在线决策支持系统以识别高质量评论。当前大多数关于有用性预测的研究都假设评论的有用性仅依赖于其自身的信息。但在实践中，消费者很难独立处理评论，因为评论是按顺序显示的；评论更有可能受到序列中相邻邻居的影响，这在很大程度上没有得到充分研究。在本文中，我们提出了第一个端到端神经架构来捕获评论与其邻居之间缺失的交互。我们的模型允许总共 12 个（三个选择 × 四个聚合）方案，将评论上下文化为从其邻居学到的上下文线索。我们根据一系列最先进的基线在六个真实世界在线评论领域评估了我们的模型。实验结果证实了连续邻居对评论的影响，并表明我们的模型明显优于基线 1% 到 5%。我们通过广泛的分析进一步揭示了评论如何在乐于助人的感知过程中受到邻居的影响。我们工作的结果和发现为评论有用性预测领域提供了理论贡献，并为实际决策支持系统设计提供了见解。我们通过广泛的分析进一步揭示了评论如何在乐于助人的感知过程中受到邻居的影响。我们工作的结果和发现为评论有用性预测领域提供了理论贡献，并为实际决策支持系统设计提供了见解。我们通过广泛的分析进一步揭示了评论如何在乐于助人的感知过程中受到邻居的影响。我们工作的结果和发现为评论有用性预测领域提供了理论贡献，并为实际决策支持系统设计提供了见解。