SocoTraveler: Travel-package recommendations leveraging social influence of different relationship types

Abstract

The immense amount of data generated and collected on e-commerce platforms provides opportunities and challenges for big data analytics to create business value. E-tourism platforms collect not only users’ travel information but also users’ social connection information and need effective personalized recommendation systems for target marketing. In this paper, we aim to study how different types of social relationships such as colleague, schoolmate, and relative between co-travelers influence a user’s travel behavior and how to use this influence to enhance recommendation quality. To this end, we develop a probabilistic topic model leveraging individual travel history and social influence of co-travelers to capture personal interests and propose a recommendation method to utilize the proposed model. Experiments on a real travel dataset show that the proposed approach significantly outperforms benchmarks. The result highlights useful findings for travel agencies.