  Most current recommender systems used the historical behaviour data of user
to predict user' preference. However, it is difficult to recommend items to new
users accurately. To alleviate this problem, existing user cold start methods
either apply deep learning to build a cross-domain recommender system or map
user attributes into the space of user behaviour. These methods are more
challenging when applied to online travel platform (e.g., Fliggy), because it
is hard to find a cross-domain that user has similar behaviour with travel
scenarios and the Location Based Services (LBS) information of users have not
been paid sufficient attention. In this work, we propose a LBS-based
Heterogeneous Relations Model (LHRM) for user cold start recommendation, which
utilizes user's LBS information and behaviour information in related domains
and user's behaviour information in travel platforms (e.g., Fliggy) to
construct the heterogeneous relations between users and items. Moreover, an
attention-based multi-layer perceptron is applied to extract latent factors of
users and items. Through this way, LHRM has better generalization performance
than existing methods. Experimental results on real data from Fliggy's offline
log illustrate the effectiveness of LHRM.
