  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.
