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

TL;DR

This paper introduces LHRM, a novel model leveraging location-based services and heterogeneous relations to improve user cold start recommendations on online travel platforms, outperforming existing methods.

Contribution

The paper proposes a LBS-based heterogeneous relations model that integrates user location and behavior data for enhanced cold start recommendation accuracy.

Findings

LHRM outperforms existing methods in real-world Fliggy data.

The model effectively utilizes LBS and behavior data for cold start scenarios.

Experimental results demonstrate improved generalization performance.

Abstract

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.