Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations
This work addresses the need for better travel decision-making by consumers, though it is incremental as it builds on existing contrastive learning methods applied to a new dataset.
The paper tackles the problem of personalized review ranking for travel accommodations by proposing a contrastive learning approach that captures reviewer context, and it demonstrates superior performance over baselines across all metrics.
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.