###### Abstract  
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.  
recommender systems; user profiling; social media; cluster analysis; association rule mining +
Footnote †: journal: Applied  
14 June 2021  
13 July 2021  
In this paper, we present a tourism recommender system that utilises features extracted from tweets of tourists in order to create user profiles which are employed to create personalised recommendations of touristic activities. The features, extracted from the activity of the users on Twitter, represent not only the users' cultural preferences, but also the context, their travel habits and the popularity of the visited _points of interest_ (POIs). We propose a recommendation algorithm that combines in a novel way clustering based on social media features with association rule mining to find the preferred combinations of POIs from those visited by similar users. These combinations capture in a novel way the relatedness of certain activities, which may be due to their similarity, their physical proximity, the ease of travel from one to the other and even their popularity. Additionally, the system ranks the mined association rules using methods that adapt to the characteristics of the user to ensure that the recommended POIs fit the user's unique interests and his/her attraction towards popular or unpopular POIs. The performance of the system has been evaluated with the initial clustering step and without it, to study the influence of the social media-based clustering in the recommendation process. The obtained results confirm the usefulness of the clustering phase to improving the performance of the recommender in several metrics.  
In summary, the main contributions of this work are the following:  
1. We introduce a method for analysing data from social media to build user profiles that encapsulate their travel preferences and habits.
2. We present insights into the potential benefits of the combination of cluster analysis and association rule mining in tourism recommenders.
3. We provide the results of in-depth experiments using a comprehensive set of numerical evaluation metrics to gauge the benefits of social media-based clustering for user profiling.  
The rest of the paper is organised as follows. Section 2 summarises works related to the construction of user profiles using information from social media and the use of clustering and association rule mining in recommender systems. Section 3 details the proposed system, including data sources, the pre-processing steps and the different stages of the recommendation process. Section 4 provides details of the experimentation, proposed evaluation and analysis of the results. Finally, Section 5 concludes the paper and outlines some possible lines of future work.