  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.
