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Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user’s preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics.

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities