WhereToGo: Personalized Travel Recommendation for Individuals and Groups

Abstract

With the rapid development of GPS-enabled mobile devices, huge amounts of user-contributed data with location information can be collected from the Internet. With this kind of data, one promising application is travel recommendation, which has attracted a considerable number of researches recently. However, most of the previous studies only focus on one aspect of the relations among users and locations or make a coarse linear combination of the relations. Moreover, all the existing work on travel recommendation do not consider recommendation to groups, which is an important characteristic of travelers' behavior. In this paper, we present a personalized travel recommendation system named Where to Go. The novelty of the system is a 3R model which can unify user-location relation, user-user relation and location-location relation into a single framework and perform random walk with restart to analyze the model. We further extend our approach to provide recommendations for groups. To the best of our knowledge, this is the first work to use random walk with restart for group recommendation. We conduct a comprehensive performance evaluation using a real dataset collected from Flickr, which is one of the most popular online photo-sharing sites. Experimental results show that our approach provides significantly superior recommendation quality compared to other state-of-the-art travel recommendation approaches for both individuals and groups.