## 4 Experiments and Results
### Experiment Results and Discussion  
We performed three identical experiments to compare the results of the recommender with and without the clustering stage (when the clustering stage was not used, all users were assumed to belong to a single unique cluster). In each experiment, the data set was randomly split into a training set (80% of 6066 users: 4853) used in the clustering and association rule mining stages, and a test set (20% of 6066 users: 1213) used to make recommendations which were evaluated with the metrics in Section 4.2. Figure 6 shows the performances of the recommender in the two cases. In almost all metrics, the use of clustering improved the performance of the system (noticeably better in some way, such as coverage and personalisation). We determined the noticeable performances by the differences between the scores in the two cases. If \(diff\geq 0.05\), it was noticeably better; if \(0.02\leq diff<0.05\) it was slightly better; and \(diff<0.02\) shows a negligible difference. The actual performance scores in the experiments are detailed in Table 6. Noteworthy _difference_ values are in bold and negligible differences are underlined. The red values show the cases in which the performance decreases when using clustering. In _UIR_, _coverageand _personalisation_, the use of clustering improves the results noticeably. These metrics benefit from the clustering process because they depend on the POI pool considered for recommendation. _Unified item recall_ and _coverage_ increased because users were placed in clusters with pools that match their preferences, so the recommended POIs were likely to have been visited. _Personalisation_ increased because the pools of different clusters were quite different, as explained in Section 4.1, so users across clusters received different recommendations, causing more uniqueness. The use of clustering provoked a slight decrease in _diversity_, due to data sparsity. _Diversity_ scored the set of recommended items based on their dissimilarity, which was affected by the _preference ratio_ in Section 3.5. In the experiments we used the categories visited by the test users as their preferred activity categories, and therefore, diversity dropped when there were clusters and the set of visited activity categories was small. This could be solved by incorporating mechanisms to enhance diversity in the system [61]. A similar drop was also seen in _average item recall_, but in this case it was due to the streamlining of the POI pools for recommendation during the ARM process. _Average category recall_ and _similarity_ were not very affected by the use of clustering; they only improved to minor extents. These metrics are largely dependent on data sparsity and the number of recommendable POIs, as detailed in Section 4.1; thus, unlike other metrics, they are indifferent to clustering. Finally, _precision_ and _popularity_ saw just noticeable increases when clustering was applied. A deeper analysis of the results is presented in Figure 7, which shows box plots of _precision_, _category recall_, _item recall_, _similarity_, _popularity_ and _diversity_ scores for each user in the test set. In experiments 2 and 3, the use of clustering was able to raise the upper quartile of the precision plot to 1, so 25% of the users in the test set were given recommendations with near 100% accuracy. However, the most important differences the plots are present for the popularity metric. In the case with clustering, although the increase in popularity in Table 6 is only slightly noticeable, the box plots show that in all the experiments the majority of the test set scored better than without clustering. The outliers shown in the box plots were responsible for the lower overall difference value. Figure 6: A bar plot of the average performance scores of the recommender system across the three experiments. Cases with and without clustering are shown in orange and blue, respectively.