Helpful reviews have been essential for the success of e-commerce services,
as they help customers make quick purchase decisions and benefit the merchants
in their sales. While many reviews are informative, others provide little value
and may contain spam, excessive appraisal, or unexpected biases. With the large
volume of reviews and their uneven quality, the problem of detecting helpful
reviews has drawn much attention lately. Existing methods for identifying
helpful reviews primarily focus on review text and ignore the two key factors
of (1) who post the reviews and (2) when the reviews are posted. Moreover, the
helpfulness votes suffer from scarcity for less popular products and recently
submitted (a.k.a., cold-start) reviews. To address these challenges, we
introduce a dataset and develop a model that integrates the reviewer's
expertise, derived from the past review history of the reviewers, and the
temporal dynamics of the reviews to automatically assess review helpfulness. We
conduct experiments on our dataset to demonstrate the effectiveness of
incorporating these factors and report improved results compared to several
well-established baselines.

针对检测有用评价的问题，本研究提出了一个新的数据集和方法，该方法利用评论者的历史评价记录和评论的时间动态来自动评估评论的有用性，并展示了比多个基准方法更好的效果。

评审人专业技能在时间性评审帮助性预测中的作用

On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction

Automatically matching reviewers to papers is a crucial step of the peer
review process for venues receiving thousands of submissions. Unfortunately,
common paper matching algorithms often construct matchings suffering from two
critical problems: (1) the group of reviewers assigned to a paper do not
collectively possess sufficient expertise, and (2) reviewer workloads are
highly skewed. In this paper, we propose a novel local fairness formulation of
paper matching that directly addresses both of these issues. Since optimizing
our formulation is not always tractable, we introduce two new algorithms,
FairIR and FairFlow, for computing fair matchings that approximately optimize
the new formulation. FairIR solves a relaxation of the local fairness
formulation and then employs a rounding technique to construct a valid matching
that provably maximizes the objective and only compromises on fairness with
respect to reviewer loads and papers by a small constant. In contrast, FairFlow
is not provably guaranteed to produce fair matchings, however it can be 2x as
efficient as FairIR and an order of magnitude faster than matching algorithms
that directly optimize for fairness. Empirically, we demonstrate that both
FairIR and FairFlow improve fairness over standard matching algorithms on real
conference data. Moreover, in comparison to state-of-the-art matching
algorithms that optimize for fairness only, FairIR achieves higher objective
scores, FairFlow achieves competitive fairness, and both are capable of more
evenly allocating reviewers.

本文提出了基于局部公平性的论文匹配新方法，并引入了两个新的算法 FairIR 和 FairFlow，相对于已有算法，实验证明 FairIR 和 FairFlow 的效果更优，能够更公平地分配评审者。