Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations

TL;DR

This paper introduces a new dataset of over two million authentic travel reviews and proposes a contrastive learning method to improve personalized review ranking, demonstrating superior performance over baselines.

Contribution

The paper provides a large-scale review dataset and a novel contrastive learning approach for personalized review ranking in the travel domain.

Findings

Our method outperforms baseline models across all metrics.

Contrastive learning effectively captures reviewer-review relationship.

Enhanced review ranking improves user decision-making.

Abstract

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.