# Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision

arXiv: https://arxiv.org/abs/1910.03943 · HTML: https://mlsanity.com/paper/1910.03943/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [ChronoR: Rotation Based Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2103.10379/) (2021)
- [Multimodal Temporal Fusion Transformers Are Good Product Demand Forecasters](https://mlsanity.com/paper/2307.02578/) (2023)
- [Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation](https://mlsanity.com/paper/2108.13824/) (2021)
- [Serving the Long Tail: Training-Free LLM Candidate Generation for Vacation Rental Marketplaces](https://mlsanity.com/paper/2607.09877/) (2026)
- [A Multi-Source Ensemble Approach to Candidate Generation for Alternative Vacation Rental Property Recommendations](https://mlsanity.com/paper/2609.05748/) (2026)

## Abstract (arXiv)

We propose a neural network architecture for learning vector representations of hotels. Unlike previous works, which typically only use user click information for learning item embeddings, we propose a framework that combines several sources of data, including user clicks, hotel attributes (e.g., property type, star rating, average user rating), amenity information (e.g., the hotel has free Wi-Fi or free breakfast), and geographic information. During model training, a joint embedding is learned from all of the above information. We show that including structured attributes about hotels enables us to make better predictions in a downstream task than when we rely exclusively on click data. We train our embedding model on more than 40 million user click sessions from a leading online travel platform and learn embeddings for more than one million hotels. Our final learned embeddings integrate distinct sub-embeddings for user clicks, hotel attributes, and geographic information, providing an interpretable representation that can be used flexibly depending on the application. We show empirically that our model generates high-quality representations that boost the performance of a hotel recommendation system in addition to other applications. An important advantage of the proposed neural model is that it addresses the cold-start problem for hotels with insufficient historical click information by incorporating additional hotel attributes which are available for all hotels.