# An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
###### Abstract
Star Trek stands out among television series for its cultural significance, number of episodes, and longevity [10] (STARFEET, The International Star Trek Fan Association, Inc. (2019) [11]). For these reasons, we elected to use Star Trek as a testbed to develop and evaluate the RSs presented in this paper. Another reason motivating our choice of Star Trek is the availability of multiple sources of information, including transcripts, user ratings, sets of tags associated with episodes, and an ontology hierarchy of tags. In particular, we used a detailed ontology of literary themes [12; 13] that was initially developed for Star Trek but has since evolved to be used for general works of fiction. This particular situation allowed us to carry out a thorough evaluation that produced insights about the usefulness of different information resources for the construction of RSs, as well as a means to evaluate a new approach for RSs based on ontology. Most state-of-the-art RSs are built using collaborative filtering (CF) [1; 3; 6], which is based solely on the analysis of user assigned item preferences. Common CF methods include the classical \(k\)-nearest neighbors algorithm [14], non-negative matrix factorization [15], and recent approaches based on efficient representations obtained using deep learning [16]. The CF approach is popular because of its high performance and independence from the nature of the items. However, it is particularly weak against the issues mentioned for the TV series domain. As an alternative, content-based filtering (CBF), knowledge-based filtering (KBF), and ontology-based filtering (OBF) approaches aim to leverage available domain knowledge in an effort to speed up the discovery of relationships between items and users, which CF approaches require large amounts of data to infer [17]. The most common role of ontology in RS design consists of providing a taxonomic classification of items [18; 19; 20]. According to this approach, user profiles are indexed by the entities from the ontology and each dimension is weighted according to the preferences of the explicit (e.g., ratings, likes) [18; 19] and/or implicit users (e.g., reads, views) [20]. Non-domain ontologies have also been used in combination with domain ontologies to aid the recommendation process, but their integration is achieved by means of rules or inference engines [21; 22; 23]. Other approaches aim to model both users and items with ontologies looking for relationships in a single semantic space [22; 24; 25]. Most of these approaches either use lightweight ontologies or use ontologies as a source for a controlled vocabulary, resulting in a shallow or null use of the ontological hierarchical structure. The exceptions are the approaches that use inference engines [18; 23]. Recent approaches [26; 27] manage to handle large ontologies while exploiting relationships along the entire hierarchy. When practitioners plan to develop a new RS, they face the problem of selecting those information resources needed to bootstrap the new system. During these initial stages, CBF/KBF/OBF approaches are to be preferred over CF alternatives, given the obvious lack of user feedback. Although there is an extensive body of literature on each of these approaches, traversal studies comparing the performances of alternative methods on a single dataset are scarce. In this paper, we aim to fill this gap and to provide practitioners with useful insights for decision-making regarding information resources. We focus on the OBF approach because this is the one where the results depend mainly on developers and because ontologies are the most informative resource to start a new RS. For instance, CBF approaches depend heavily on the nature of the items (e.g., books, hotels, clothing, movies), whose native data representations are not always RS friendly. Similarly, CF approaches depend on the number of users, which in turn depends on external factors such as popularity, advertising, trends, etc.