# A Novel Hybrid Recommender System for the Tourism Domain
## 5 Experimental Evaluation and Results
### Evaluation of the Complete Hybrid Recommender System  
In this section we evaluate the complete _Hybrid_ recommender algorithm. As explained earlier, we now adopt the iterative elicitation process introduced in Section 4.1, which results in a multivariate Gaussian distribution, \(\mathcal{N}(\mu_{u},\Sigma_{u})\), that describes the preferences of user \(u\). In detail, we run a series of user-system interactions with varying \"slates\". A slate in our case corresponds to an implicit \"query\" presented to the user, comprising of a number \(n\) of images for the user to select a preferred one from. We experiment with varying numbers of slates \(m=\{4,5,6\}\) posed to the user, and with varying numbers \(n=\{4,5,6\}\) of generic images presented to the user in a slate (\"query\"). The testing of various such \(m,n\) combinations of _elicitation slates, images shown per slate_ pairs, aims to assess the trade-off between the information offered to the algorithms (expected, naturally, to improve their performance), and the frustration caused to users when the time required from them to spend \"marking\" (generic) images for elicitation purposes increases [40]. Thus, as also explained in Section 4.1, during each \(\langle m,n\rangle\) elicitation instance, the user selects the generic images that are more appealing to her, given her interests. The \"selected\" images are stored in the \(u_{L}\) set, and used alongside \(\mu_{u}\) to recommend POIs to the user (as explained in Figure 7 and Algorithm 2). In Tables 4-12 we present the average values of precision and recall, alongside with the (average) number of relevant POIs, \(Rec_{r}\) (the average number of relevant POIs among the 20 recommended) and _Recall UB_ for all age groups, each of which comprises 100 users who enter the system 30 times. Furthermore, we illustrate the average precision values for every \(\langle m,n\rangle\) combination in Table 13.  
As seen in Table 13, our _Hybrid RS_ outperforms the aforementioned _CB_ algorithms regarding the average values of _Precision_ for all pairs of \(\langle m,n\rangle\), and also almost always outperforms them in terms of _Recall_ (the latter's values are regularly about 85% of those of _Recall UB_, i.e., our method regularly returns about 17 recommendations that are relevant to the user profile, out of the 20 relevant recommendations that it could have possibly returned). However, slightly higher precision outcomes are observed for some age groups (namely 56-67, 67+) for the _Hybrid SMbR_ CB recommender (see Table 2). Overall, it is clear that the _Hybrid RS_ has a much more stable performance than the CB recommenders, achieving outcomes that are consistently higher than 0.84 for all pairs (apart from the 67+ age group), and consistently including about 17 POIs that are relevant to the user interests, in its 20 returned recommendations. \begin{table}
\begin{tabular}{c c} _Hierarchy_ **SMbR** & _Hybrid_ **SMbR** \\ \hline
0.7911 & 0.7913 \\ \end{tabular}
\end{table} 
Table 3: Average precision values for the CB recommendation methods. According to Table 13 our _Hybrid_ recommender achieves the best results for its variant using values of \(m=4,\ n=6\) during elicitation--i.e., when the user is presented four times with six images each time. Now, for larger values of combinations \(m,n\), the cardinality of the set \(u_{L}\) is larger (\(|u_{L}|\geq m\)). This creates the possibility for more irrelevant POIs being contained in the sets \(u_{M}\) and \(u_{M^{\prime}}\), and as a result they may be presented in the final recommendations to the user-- naturally, when the proportion of the irrelevant POIs is high, the precision results decrease. We can see a possible manifestation of this behaviour in the precision outcome of the \(m=6\), \(n=6\) pair, which is lower than the respective \(m=4,\ n=6\), even though more information is potentially elicited from the user in the \(m=6,\ n=6\) case. That information probably results to a \"blurring\" of the user model.