# Rakuten Data Release: A Large-Scale and Long-Term Reviews Corpus for Hotel Domain

arXiv: https://arxiv.org/abs/2512.15151 · HTML: https://mlsanity.com/paper/2512.15151/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [The Multilingual Amazon Reviews Corpus](https://mlsanity.com/paper/2010.02573/) (2020)
- [Joint Optimization of Tokenization and Downstream Model](https://mlsanity.com/paper/2105.12410/) (2021)
- [Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations](https://mlsanity.com/paper/2407.00787/) (2024)
- [SymCERE: Symmetric Contrastive Learning for Robust Review-Enhanced Recommendation](https://mlsanity.com/paper/2504.02195/) (2025)
- [Comprehensive Evaluation on Lexical Normalization: Boundary-Aware Approaches for Unsegmented Languages](https://mlsanity.com/paper/2505.22273/) (2025)

## Abstract (arXiv)

This paper presents a large-scale corpus of Rakuten Travel Reviews. Our collection contains 7.29 million customer reviews for 16 years, ranging from 2009 to 2024. Each record in the dataset contains the review text, its response from an accommodation, an anonymized reviewer ID, review date, accommodation ID, plan ID, plan title, room type, room name, purpose, accompanying group, and user ratings from six aspect categories, as well as an overall score. We present statistical information about our corpus and provide insights into factors driving data drift between 2019 and 2024 using statistical approaches.