# A Novel Hybrid Recommender System for the Tourism Domain
## 2 Background and Related Work  
Recommender systems are considered to be personalized and non-personalized [4; 23]. Personalized recommender systems extrapolate user's preferences to create efficient recommendations based on the users' past interaction with the system [8]. On the contrary, non-personalized recommenders suggest items that are most relevant and popular among all users. For instance the Netflix [24] movie platform recommends the top N items to every user as a list. 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, [PERSON] 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 [PERSON] 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  
Figure 2: Our _Visit Planner (ViP)_ real-world RS mobile application: Screenshots. 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. We now proceed to briefly review several such systems and algorithms, along with certain recommender approaches that may lie outside the tourism domain but are of interest to our work in this paper. 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. [PERSON] 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. [PERSON] et al.