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.

本研究基于 hotel2vec 模型，提出了一种简单的正则化方法，通过域自适应对不同品牌的酒店嵌入进行对齐，并探讨了在跨语言嵌入中使用的对齐方法。结果表明，该方法可以在两个品牌之间对齐两个嵌入空间，并在两个品牌中均实现良好的性能表现。此外，相对于单品牌训练，该方法可以显著减少训练时间并提高预测性能。

利用领域自适应技术对酒店嵌入进行匹配，用于下一个项目推荐