User-generated reviews significantly influence consumer decisions, particularly in the travel domain when selecting accommodations. This paper contribution comprising two main elements. Firstly, we present a novel dataset of authentic guest reviews sourced from a prominent online travel platform, totaling over two million reviews from 50,000 distinct accommodations. Secondly, we propose an innovative approach for personalized review ranking. Our method employs contrastive learning to intricately capture the relationship between a review and the contextual information of its respective reviewer. Through a comprehensive experimental study, we demonstrate that our approach surpasses several baselines across all reported metrics. Augmented by a comparative analysis, we showcase the efficacy of our method in elevating personalized review ranking. The implications of our research extend beyond the travel domain, with potential applications in other sectors where personalized review ranking is paramount, such as online e-commerce platforms.

翻译：用户生成的评论显著影响消费者决策，尤其在旅行领域选择住宿时。本文的贡献包含两个主要部分。首先，我们提出了一个从知名在线旅行平台获取的真实旅客评论数据集，包含来自50,000个不同住宿的超过两百万条评论。其次，我们提出了一种创新的个性化评论排序方法。该方法采用对比学习来精细捕捉评论与其对应评论者上下文信息之间的关系。通过全面的实验研究，我们证明了该方法在所有报告指标上均优于多个基线模型。借助对比分析，我们展示了本方法在提升个性化评论排序效果方面的有效性。本研究的意义不仅限于旅行领域，还可应用于其他个性化评论排序至关重要的场景，例如在线电子商务平台。