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Personalized Attraction Recommendation System for Tourists Through Check-In Data

Online social networks now play a prominent role in our daily lives and our decisions and behaviors in many areas. Of particular interest here is the application of social network data to give users access to tourist information. There is a growing need for information on tourism and tourist activities to satisfy user queries in this domain. Social networks, such as Facebook, Twitter, and Foursquare, among others, store substantial volumes of check-in data, which are a valuable resource for recommending tourism attractions. However, using Facebook check-in data has rarely been considered in conventional recommendation systems (RSs). This presents not only a new research challenge for the computer science and information technology fields but also an interesting opportunity for the tourism industry: knowing what kind of attractions tourists are interested in and how to acquire their user preferences without adding tasks to users of an RS. We propose a tourism RS that is based on its recommendations on data dynamically aggregated and extrapolated from the Facebook check-in data. In addition, the so-called “cold-start” problem has been resolved by using users’ Friends’ check-in data to analyze ongoing Facebook activity and update user profiles in the system. Most Facebook users have a well-extended list of Friends. Consequently, the proposed system can dynamically learn user behavior and appropriately adapt recommendations. This paper demonstrate the usefulness of the data available on Facebook through the example studies involving attraction recommendations, resolving the cold-start problem, and adapting the user model to improve recommendation quality in the tourism domain.