# A Novel Hybrid Recommender System for the Tourism Domain
## 1 Introduction  
Tourism is a widespread activity and a huge industry in our time and era. According to the United Nations' World Tourism Organization, more than 900 million tourists travelled internationally in 2022 alone. In general, tourism can have a great impact on various aspects of social and economic life--including local infrastructure, transportation, land use, housing prices and even local lifestyle and associated entrepreneurial activities [1; 2; 3]. In their travels, tourists have to select among numerous alternatives of landmarks, places and in general _points of interest_ (POIs) they can visit. This can be challenging for travellers that have no prior experience of or \"inside knowledge\" regarding their destination, and even more so for short-term visits. The exhaustive examination of all the possibilities is clearly impossible, thus an information system that can assist them in narrowing their options down to a more tight set would be of much use to tourists. As a result, a booming industry of travel-related _recommender systems_ (RSs) [4] has been developed, in order to provide users with recommendations most relevant to their interests. Such systems are embedded in almost every modern mobile tour guide and similar applications. Recommender systems are divided into three main categories with respect to the methods they adopt, namely the _content-based_ (CB), _collaborative filtering_ (CF) and _hybrid_ RSs [4; 5; 6]. Nevertheless, they can further be grouped into additional categories, such as _Bayesian recommenders_, which employ Bayesian updating of user models for efficient personalized recommendations [7; 8; 9]. A recent research work on _travel recommenders_[10] has classified these into three major categories: hotel, restaurant and tourism recommenders. The latter can be related to tour planning, group recommendations, touristic attractions-related packages or travel packages. Recently, Bayesian recommender algorithms have been combined with social choice-theoretic methods to enable personalized and fair recommendations of touristic POIs, both in single-user and group recommendation environments [11; 12]. Now, although _semantic similarity measures_ have been mainly exploited in the domains of text analysis [13] and natural language processing [14], they have also been employed to correlate the preferences of tourists with touristic attractions. All semantic similarity measures describe the relations between different ontologies, and are classified into two categories [15]. The first corresponds to those that generate a hierarchical structure in order to measure similarity and are termed _hierarchy similarity measures_, while the second correlates ontologies without constructing a hierarchy tree and are termed _non-hierarchy similarity measures_. Against this background, in this paper we put forward a novel recommender algorithm that is a _hybrid_ of two approaches: a novel content-based (CB) recommender utilizing semantic similarity measures, and a Bayesian one. In fact, our CB recommender algorithm is itself, in some sense, a \"hybrid\" of two (CB) approaches that employ two types of semantic similarity measures--a hierarchy and a non-hierarchy one. The hierarchy similarity measure of choice is the _Extended Wu-Palmer_ (_XWP_) similarity, a variation of the metric proposed in [16], in which POIs lie on the nodes of a hierarchy tree, whereas the other metric is a novel non-hierarchy similarity measure called _Weighted Extended Jaccard Similarity_ (_WEJS_) measure. _WEJS_ calculates the correlation between different POIs by taking into account both the user's preferences and the POIs' features values. Thus it produces personalized recommendations. Subsequently, our CB algorithm combines these two measures into a _hybrid_ (the term hybrid here denotes a combination of two similarity measures and it should not be confused with the hybrid recommender algorithm that is the final outcome of our work) similarity measures-based method.