利用评论邻居进行情境化的有用性预测
Exploiting Review Neighbors for Contextualized Helpfulness Prediction
摘要：
有用性预测技术已被广泛用于识别并向客户推荐高质量的在线评论。目前，绝大多数的研究都认为评论的帮助是独立的。然而，在实践中，考虑到顺序性，客户很难独立地处理评论。评论的感知有用性可能会受到其连续邻居的影响（即，），这在很大程度上被忽视了。本文提出了一种新的方法来捕捉评论及其邻居之间缺失的交互。第一个端到端的神经架构是为邻居感知的帮助预测（NAP）开发的。对于每次查看，NAP允许三种类型的邻居选择：它的前邻居、后邻居和周围邻居。四个加权方案的目的是从选定的邻居学习上下文线索。然后，将一个评论情境化到邻居意识的有用性预测的学习线索中。根据一系列最先进的基线，在六个真实世界在线审查领域对国家行动方案进行评估。大量的实验证实了NAP的有效性和顺序邻居对当前评论的影响。进一步的超参数分析揭示了三个主要发现。(1)平均而言，八个邻居对待不均匀的重要性从事上下文建设。(2)邻居感知预测的好处主要来自更近的邻居。(3)同样地，考虑评论的最多五个最近邻居通常可以产生较弱但可容忍的预测结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
