# HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset

arXiv: https://arxiv.org/abs/2002.06854 · HTML: https://mlsanity.com/paper/2002.06854/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [RecGPT: Generative Pre-training for Text-based Recommendation](https://mlsanity.com/paper/2405.12715/) (2024)
- [Fast Multi-Step Critiquing for VAE-based Recommender Systems](https://mlsanity.com/paper/2105.00774/) (2021)
- [A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions](https://mlsanity.com/paper/2506.17285/) (2025)
- [RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation](https://mlsanity.com/paper/2509.03131/) (2025)
- [ReviewGraph: A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features](https://mlsanity.com/paper/2508.13953/) (2025)
- [Doc2AHP: Inferring Structured Multi-Criteria Decision Models via Semantic Trees with LLMs](https://mlsanity.com/paper/2601.16479/) (2026)
- [HotelQuEST: Balancing Quality and Efficiency in Agentic Search](https://mlsanity.com/paper/2602.23949/) (2026)
- [ViHoRec: A Quality-Controlled Vietnamese Hotel Recommendation Dataset and Cold-Start Benchmark](https://mlsanity.com/paper/2607.12946/) (2026)
- [Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts](https://mlsanity.com/paper/2104.12822/) (2021)
- [Positive and Negative Critiquing for VAE-based Recommenders](https://mlsanity.com/paper/2204.02162/) (2022)
- [Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen](https://mlsanity.com/paper/2208.12356/) (2022)
- [Tracking Brand-Associated Polarity-Bearing Topics in User Reviews](https://mlsanity.com/paper/2301.07183/) (2023)
- [Cone: Unsupervised Contrastive Opinion Extraction](https://mlsanity.com/paper/2305.04599/) (2023)
- [Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences](https://mlsanity.com/paper/2605.16615/) (2026)

## Abstract (arXiv)

Today, recommender systems are an inevitable part of everyone's daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models require a large number of data to achieve their best performance. Many datasets fulfilling this criterion have been proposed for multiple domains, such as Amazon products, restaurants, or beers. However, works and datasets in the hotel domain are limited: the largest hotel review dataset is below the million samples. Additionally, the hotel domain suffers from a higher data sparsity than traditional recommendation datasets and therefore, traditional collaborative-filtering approaches cannot be applied to such data. In this paper, we propose HotelRec, a very large-scale hotel recommendation dataset, based on TripAdvisor, containing 50 million reviews. To the best of our knowledge, HotelRec is the largest publicly available dataset in the hotel domain (50M versus 0.9M) and additionally, the largest recommendation dataset in a single domain and with textual reviews (50M versus 22M). We release HotelRec for further research: https://github.com/Diego999/HotelRec.