# Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation

arXiv: https://arxiv.org/abs/2108.13824 · HTML: https://mlsanity.com/paper/2108.13824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [Item2Vec: Neural Item Embedding for Collaborative Filtering](https://mlsanity.com/paper/1603.04259/) (2016)
- [Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification](https://mlsanity.com/paper/1606.01614/) (2016)
- [DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns](https://mlsanity.com/paper/1905.10760/) (2019)
- [Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation](https://mlsanity.com/paper/1607.07326/) (2016)
- [E-commerce in Your Inbox: Product Recommendations at Scale](https://mlsanity.com/paper/1606.07154/) (2016)
- [Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario](https://mlsanity.com/paper/2007.14906/) (2020)
- [Cross-domain Recommendation via Deep Domain Adaptation](https://mlsanity.com/paper/1803.03018/) (2018)
- [One Embedding To Do Them All](https://mlsanity.com/paper/1906.12120/) (2019)
- [Compass-aligned Distributional Embeddings for Studying Semantic Differences across Corpora](https://mlsanity.com/paper/2004.06519/) (2020)
- [Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision](https://mlsanity.com/paper/1910.03943/) (2019)

## Abstract (arXiv)

In online platforms it is often the case to have multiple brands under the same group which may target different customer profiles, or have different domains. For example, in the hospitality domain, Expedia Group has multiple brands like Brand Expedia, Hotels.com and Wotif which have either different traveler profiles or are more relevant in a local context. In this context, learning embeddings for hotels that can be leveraged in recommendation tasks in multiple brands requires to have a common embedding that can be induced using alignment approaches. In the same time, one needs to ensure that this common embedding space does not degrade the performance in any of the brands. In this work we build upon the hotel2vec model and propose a simple regularization approach for aligning hotel embeddings of different brands via domain adaptation. We also explore alignment methods previously used in cross-lingual embeddings to align spaces of different languages. We present results on the task of next-hotel prediction using click sessions from two brands. The results show that the proposed approach can align the two embedding spaces while achieving good performance in both brands. Additionally, with respect to single-brand training we show that the proposed approach can significantly reduce training time and improve the predictive performance.