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 entrepreneurship 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.

Recommender systems may also be classified into several other types: for example, content-based (CB), collaborative filtering (CF), context-aware, Bayesian—while the term “Hybrid” characterizes a recommender that produces recommendations via combining algorithms that belong in several categories. Usually CB and CF are seen as constituting the two main (non-hybrid) approaches [4,5].

In a nutshell, CB RSs exploit information from previous user–system interactions to provide effective recommendations. Particularly, these systems suggest items that are very similar to items the user has liked in previous interactions. CF algorithms, by contrast, analyze user’s ratings to calculate the similarity between them. CF recommenders intuitively work under the assumption that, when items are rated by two users in a similar way, they probably share same interests and will provide similar ratings to other items. As mentioned in the introduction, the cold-start problem refers to the inability of such algorithms to make recommendations which are personalized and relevant to users’ preferences, due to the lack of ratings when dealing with new users or items-to-recommend [19]; this problem is of vital importance in applications related to the tourism domain [20], and thus several ways to tackle cold-start in this domain are proposed in the literature. For instance, Feng et al. [19] propose a ranking model which is a hybrid between a CF ratings-oriented and a Bayesian personalized pairwise ranking-oriented one, while Zheng et al. [20] employ a hybrid CF-based method that refines item opinion reputation and user preferences, by utilizing opinion-mining technology to mine text reviews and subsequently assess the destinations’ preference ranking when matched against preferred features chosen by users via means of an artificial interaction module.

Now, there are numerous travel- or tourism-related RSs, which may be classified into several categories [10]. The majority of these systems suggest POIs that relate to tourist attractions (e.g., monuments, museums or hotels), ideally those that are closely related to the preferences of each visitor while, naturally, in terms of underlying recommendation technology used, a tourism RS may belong to any one of the aforementioned RS classes. 

To begin with, a recommender algorithm, along with a social interactions mechanism, is equipped in a tourist guide application, presented by [25], with the goal of locating undiscovered touristic POIs. An integrated CF system is employed to suggest touristic locations that have been already rated by the users. Lim et al. [26] tackle a tour itinerary planning problem modelled in the context of the well-known orienteering problem, and propose several recommendation algorithm variants that take into account POI popularity to a lesser or greater extent, while assuming user preferences relate to the user’s visit durations at POIs of particular categories. The authors of [27] proposed a picture-based recommender technique for proposing tourism sites to a specific individual. Particularly, any set of images is picked by the user and then is imported into computer vision models that generate a profile with respect to the tourist’s interests. Sarkar et al. [28] introduced a Crow Search Optimization-based Hybrid Recommendation model capable of generating precise recommendations to travelers though the combination of CB and CF techniques. In their algorithm, undiscovered items are presented to a user based on similar item selection from past interactions. Thus, the similarity between the items is calculated via a combined employment of Jaccard Similarity and Simple Matching Coefficient (SMC) as similarity metrics. In addition, their method is improved as Collaborative Filtering for Java (CF4J) and is enhanced with the Jaccard Similarity metric. They assess experimentally their approach through data provided by the well-known travel-related platform TripAdvisor. Riyani et al. [29] introduced a hybrid RS that works under implicit ratings and semantic similarity in order to provide effective recommendations in a different domain. In greater detail, their suggested method is divided into three filtering components: content-based, collaborative and hybrid, and it makes use of tagging attributes to provide more relevant suggestions on discussion groups. The WordNet lexical database [30] is used to extract the semantic importance of the tags, which are then grouped in a hierarchical framework depending on their semantic relevance.

Now, Bayesian recommenders explicitly model their underlying uncertainty regarding user preferences by efficiently maintaining and exploiting prior distributions over their user models, with the purpose of progressively improving the accuracy of their recommendations. Specifically, Bayesian methods have been proved quite significant in tackling uncertainty in applications with implicit feedback. In [31] researchers derived a generic optimization criterion using a Bayesian analysis of the problem and presented a learning algorithm which is able to provide solutions which satisfy the aforementioned criterion. Sun et al. [32] demonstrated a method using Bayesian Graph Convolutional Neural Networks for modeling the interaction of users with items in implicit recommendations setting. Nevertheless, Bayesian approaches are of course able to deal with and improve recommendations where users share explicit feedback to the system.

For instance, concerning the domain of our interest, the authors of [18] focus on travel personalized recommendations and demonstrate interesting applications by utilizing freely available community-contributed photos. In more detail, they introduce a probabilistic Bayesian framework for mobile recommendations and test it on over ten million images gathered from 19 large cities. Furthermore, Babas et al. [8] propose a Bayesian approach that models both the items under recommendation and the user preferences by the same underlying distribution. In their case, this distribution was the multivariate Gaussian distribution. This is what they term as the “You Are What You Consume” concept. Their recommendation movie technique exhibits performance results that are comparable to the (at the time) state of the art of a popular CF method for movie recommendations, as shown via an experimental evaluation on data from the MovieLens dataset. It is interesting that this is achieved without the approach having to consult previously obtained or processed data about the user. We adopt certain aspects of their approach during the preference elicitation process of our Hybrid RS.

Finally, although providing solutions to a different application domain, because of the fact that it utilizes semantic similarity measures and hierarchies of items-to-recommend, is that of [33]. The authors, in particular, provide a museum RS for cellphones that merges a CB approach with semantic similarity measures and a semantically enriched CF method to propose relevant museum exhibits to the visitors. Contextual post filtering is implemented to create a personalized tour of the museum depending on the physical environment and location of the visitor. In opposition to our approach, the authors only employ Wu–Palmer and Jaccard similarity metrics; they do not utilize Bayesian inference. They limit themselves to museum collections since their method is strongly dependent on the usage of specialized ontologies for artworks and cultural heritage, while that paper comes without any kind of evaluation for the approach it proposes.