# A Novel Hybrid Recommender System for the Tourism Domain
## 2 Background and Related Work
[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. [PERSON] 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. [PERSON] 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, [PERSON] 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, a work that bears certain similarities with our, 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.