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Most current recommender systems used the historical behaviour data of userto predict user' preference. However, it is difficult to recommend items to newusers accurately. To alleviate this problem, existing user cold start methodseither apply deep learning to build a cross-domain recommender system or mapuser attributes into the space of user behaviour. These methods are morechallenging when applied to online travel platform (e.g., Fliggy), because itis hard to find a cross-domain that user has similar behaviour with travelscenarios and the Location Based Services (LBS) information of users have notbeen paid sufficient attention. In this work, we propose a LBS-basedHeterogeneous Relations Model (LHRM) for user cold start recommendation, whichutilizes user's LBS information and behaviour information in related domainsand user's behaviour information in travel platforms (e.g., Fliggy) toconstruct the heterogeneous relations between users and items. Moreover, anattention-based multi-layer perceptron is applied to extract latent factors ofusers and items. Through this way, LHRM has better generalization performancethan existing methods. Experimental results on real data from Fliggy's offlinelog illustrate the effectiveness of LHRM.

LHRM: A LBS based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform