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

arXiv: https://arxiv.org/abs/2407.00787 · HTML: https://mlsanity.com/paper/2407.00787/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 30 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Contrastive Learning for Recommender System](https://mlsanity.com/paper/2101.01317/) (2021)
- [SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning](https://mlsanity.com/paper/2209.05040/) (2022)
- [On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction](https://mlsanity.com/paper/2303.00923/) (2023)
- [Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank](https://mlsanity.com/paper/1806.03555/) (2018)
- [Personalized Review Ranking for Improving Shopper's Decision Making: A Term Frequency based Approach](https://mlsanity.com/paper/2009.03258/) (2020)

## Built on by (most cited first, 2 of 2)

- [Rakuten Data Release: A Large-Scale and Long-Term Reviews Corpus for Hotel Domain](https://mlsanity.com/paper/2512.15151/) (2025)
- [Improving User Experience with Personalized Review Ranking and Summarization](https://mlsanity.com/paper/2601.05261/) (2025)

## Abstract (arXiv)

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.