# A Novel Hybrid Recommender System for the Tourism Domain
## 5 Experimental Evaluation and Results
### Evaluation of Our Content-Based Recommendation Algorithms  
We first conduct a series of experiments in order to evaluate the performance of the semantic similarity-based algorithms that are contained in the CB component of our system--i.e., the methods introduced in Sections 3.2 and 3.3 of this paper. Since our experiments involve synthetic users, our CB recommendation process requires the identification of images a synthetic user \"likes\". This is carried out by comparing the _cosine similarity among the values of the (twelve) features of the _\"actual\"_ (synthetic) user profile and the values of the corresponding features representing the (fifteen) \"generic\" images displayed to the user by the elicitation component (termed \"Show Generic Images\" in Figure 3) of the CB recommendation process. In case the cosine similarity between an image and the profile exceeds a threshold, set to 0.80, the image is considered to be \"selected\" (or \"liked\") by the user; otherwise it is not. The \"liked\" images make up the \(u_{L}\) set of images fed into the _Hierarchy SMbR_, while the _Build User Profile_ sub-component (cf. Figure 3) also constructs a _user profile_ that the (hybrid) CB recommender can use for its recommendations. We now proceed with the comparison between the performance of the _hierarchy SMbR_ algorithm (i.e., the output of the _Normalization Process_ in blue color in Figure 5) and the _hybrid SMbR_ (i.e., the output of the _Normalization Process_ in black color in Figure 5). In Tables 1 and 2 we illustrate the aforementioned measures of quality (i.e., precision and recall) of our application, together with the number of relevant POIs, \(Rec_{r}\) and _Recall UB_ for each age group. (We remind the reader that Recall UB (i.e., Recall Upper Bound) stands for the maximum possible value of Recall that could be attained by an algorithm capable of \"optimally\" eliciting and exploiting users' preferences in this setting, i.e., no algorithm could have performed better than this value.) The results of Table 1 concern the _Hierarchy_ SMbR, while those of Table 2 correspond to the _Hybrid_ SMbR of the CB recommender. We observe that the results (i.e., the values of _Precision_, and the respective values of _Recall_ compared to _Recall UB_) between the two recommendation processes are quite satisfactory, as _Precision_ is regularly higher than or close to 80%, while _Recall_ reaches values close to or higher than 80% of the respective _Recall UB_ ones. In general, the two _CB_ methods appear to be comparable to each other: _Hierarchy SMbR_ achieves a better performance for younger users, while Hybrid SMbR attains greater values of precision and recall for the age groups 56-67 and 67+. In addition, Table 3 illustrates the average precision of the algorithms across all age groups. We can observe that the _Hybrid SMbR_ has an average precision value that is slightly greater than the one of the _Hierarchy SMbR_. Of course, this small difference in precision alone would not be enough in order to pick the _Hybrid SMbR_ as the preferable CB recommender system between the two. However, another drawback of the _Hierarchy SMbR_ is that the \(u_{M}\) set is always the same for all users who \"liked\" a particular generic image, as such the generated recommendations would be quite similar for these users. Moreover, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{_Hierarchy Similarity Measure-Based Recommendations_} \\ \hline
**Age Group** & **Precision** & **Recall** & **Recall UB** & **Relevant Items** & \(Rec_{r}\) \\ \hline
**17–25** & 0.782 & 0.057 & 0.073 & 272.4 & 15.6 \\
**26–35** & 0.817 & 0.059 & 0.072 & 275.3 & 16.2 \\
**36–45** & 0.781 & 0.057 & 0.072 & 274.2 & 15.6 \\
**46–55** & 0.821 & 0.060 & 0.074 & 269.3 & 16.4 \\
**56–67** & 0.862 & 0.063 & 0.073 & 272.9 & 17.2 \\
**67+** & 0.684 & 0.055 & 0.080 & 248.9 & 13.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Hierarchy_ SMbR evaluation results.