# A Novel Hybrid Recommender System for the Tourism Domain
## 3 Content-Based Recommender Component
### Hybrid Similarity Measure-Based Recommendations  
However, the _Hierarchy SMBR_ sub-component need not operate alone, but can be extended to our (\"hybrid\") _CB recommender_ as follows. We now add a sub-component that performs _Hybrid similarity measure-based recommendations_ (right part of Figure 5), and which incorporates a non-hierarchy similarity measure, the Weighted Extended Jaccard Similarity (_WEJS_) [22], combining it with the (hierarchy-based) \(XWP\). In detail, as illustrated in Figure 5, the _Hybrid SMbR_ sub-component takes three different inputs: (i) the \(u_{M}\) sets produced by _Hierarchy SMBR_ (we remind the reader that each such set corresponds to a generic image in \(u_{L}\)); (ii) the _user profile vector_ (i.e., the 12 feature-based vector generated by the _Build User Profile_ component); and (iii) the POIs included in the database alongside their features. Our system transforms the \(XWP\) similarity outcomes into probability distributions for each \(u_{M}\) produced by the hierarchy similarity measure via the _Normalization & Sampling to Output_ component. Specifically, after this transformation, each \(XWP\) similarity score of a POI in a specific \(u_{M}\) set is mapped to a value in the range of [0,1], while the summation of all such values for POIs in the \(u_{M}\) in question equals 1. Given these probabilities, we sample out of each \(u_{M}\) (corresponding to an image in \(u_{L}\)), one POI for computing its _WEJS_ similarity score (via the Equation (4) formula explained below in detail). As Figure 5 indicates, we denote the drawn POI of \(u_{M}^{i}\) set as \(POI_{M}^{i}\), where \(i\in\{1,\cdots,N\}\). After the employment of _WEJS_ similarity score, for each \(POI_{M}^{i}\), a new set, denoted as \(u_{M^{\prime}}^{i}\), is generated, and it contains at most \(M^{\prime}\) similar POIs  
Figure 5: Overview of our Content-based (CB) Recommender. along with their corresponding \(WEJS\) similarity scores. Following this, all generated sets \(u_{M^{i}}^{i}\), \(i\in\{1,\cdots,N\}\), are concatenated, and their \(WEJS\) similarity scores are converted into probabilities. Finally the system samples out 20 POIs, and recommends them to the user. Now, \(WEJS\) is a novel metric computing the relevance of sets of ontologies inspired by the well-known _Jaccard Similarity (JS)_ one, which is calculated by this formula:  
\[JS(X,Y)=\frac{|X\cap Y|}{|X\cup Y|}, \tag{3}\]  
where \(|X\cap Y|\) represents the cardinality of the intersection of sets \(X\) and \(Y\), while \(|X\cup Y|\) constitutes the size of the union of the aforementioned sets. \(JS\) is zero when the compared sets are disjoint, while it is equal to one when the sets are identical. On the other hand, \(WEJS\) is computed by the following expression:  
\[WEJS(X,Y)=\frac{\sum_{i\in X\cap Y}\alpha\cdot w_{i}}{\sum_{j\in X\cup Y}w_{j} },\quad\alpha= \begin{cases}1,&\text{if}\quad v_{i}^{X}=v_{i}^{Y}\quad\quad with \quad\quad i\in X\cap Y\\ 0,&\text{otherwise}.\end{cases}  \tag{4}\]  
where \(X\) and \(Y\) in our application domain correspond to two distinct POIs--i.e., to the sets of these POIs' feature values. Moreover, \(v_{i}^{X}\) and \(v_{i}^{Y}\) represent the value of the \(i^{th}\) feature of \(X\)'s and \(Y\)'s intersection; thus, \(\alpha\) is 1 if they are identical, otherwise it is 0. \(w_{i}\) (respectively \(w_{j}\)) is given by the output of Algorithm 1, and corresponds to the weight of the \(i^{th}\) (\(j^{th}\)) feature of the intersection (union) of POIs \(X\), \(Y\). As such, \(WEJS\) measure exploits the generated weights of all POI's last 44 features (i.e., _characteristics_) with the purpose of comparing the intersection members (i.e., features) of two POIs, and it utilizes only those with equal values.