## 5 Conclusions and Future Work  
In this paper, we have proposed a cluster-based user profiling technique combined with association rule mining to recommend points of interest to tourists. This technique focuses on user features extracted from social media that represent interests, habits and context. The intention of the clustering step is not to obtain any predefined number of classes of users, but rather to learn abstract generalisations implicit in the data set.  
The resulting user profiles are then filtered using association rule mining to recommend touristic activities with high affinity. We evaluated the approach with geolocated tweets from Twitter, posted in the city of Barcelona in 2019, and performed a comparative analysis of the results with and without the clustering technique. We conclude that the clustering approach improves the system's accuracy and its ability to encapsulate a user's interests, by refining the pool of recommendable items to a user with the help of the association rules of his/her associated cluster.  
In the recommendation process, the initial data collection and pre-processing steps were the most difficult ones, due to gaps in the Twitter data, especially in cases where tweets were not geolocated. This created a partial picture when analysing user preferences. It was also challenging to find a suitable number of clusters in the user profiling step. In addition, the unexpected negative effect of COVID-19 restrictions on tourism left 2019 as the only viable year for the analysis. Finally, we were unable to recommend diverse items without explicitly defining diversity mechanisms in the system.  
In the future, we plan to incorporate more user attributes and context into the clustering features. We will consider categorical features and experiment with k-mediods as the clustering algorithm. We also plan to add an extra step, wherein we order the recommended touristic activities to improve tourist satisfaction by viewing this as an orienteering problem [62]. Finally, we will incorporate the system into a real-world application to enact more testing and fine tuning.  
Python code for all algorithms and the tweet IDs for the dataset are available at [[https://www.mdpi.com/article/10.3390/app11146512/s1](https://www.mdpi.com/article/10.3390/app11146512/s1)]([https://www.mdpi.com/article/10.3390/app11146512/s1](https://www.mdpi.com/article/10.3390/app11146512/s1)).  
Conceptualisation, J.A.O., J.B. and A.M.; methodology, J.A.O.; software, J.A.O.; validation, J.B. and A.M.; formal analysis, J.A.O.; investigation, J.A.O.; resources, J.B. and A.M.; data curation, J.A.O. and J.B.; writing--original draft preparation, J.A.O.; writing--review and editing, J.B. and A.M.; visualization, J.A.O.; supervision, J.B. and A.M.; project administration, J.B. and A.M.; funding acquisition, J.B. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
Data Availability Statement: Third Party Data Restrictions apply to the availability of these data. Data were obtained from Twitter and are available at [[https://twitter.com](https://twitter.com)]([https://twitter.com](https://twitter.com)) (accessed on 1 January 2019) with the permission of Twitter. Researches are allowed to provide Tweet IDs to other researchers to download using Twitter's API. The Tweet IDs of data presented in this paper are included in the Supplementary Material.  
Acknowledgments: [PERSON] is a fellow of Eurecat's \"[PERSON]\" PhD grant program.  
Conflicts of Interest: The authors declare no conflict of interest.