# An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
###### Abstract  
Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content. keywords: knowledge-based recommender systems; knowledge representation; literary theme; ontological engineering; ontology population; ontology-based recommender systems; Star Trek +
Footnote †: journal:  
0000-0001-6999-4300-3300]M. A. A.  
RSs have received special attention in the television domain over the last two decades (see [3] for an extensive survey), due in part to a rapid increase in the production of scripted TV series in conjunction with an online streaming service boom [4]. One issue concerning scripted TV series is that, in most RSs, items are considered at \"channel\" or \"program\" level [3]. As a consequence, scripted TV series consisting of dozens or even hundreds of episodes get treated as single items [5]. In addition, RSs applied to the television domain face the general item cold-start problem [6], as well as the particular issue of scripted TV series consisting of sets of mostly similar items (i.e., episodes) that are difficult to differentiate among for the purpose of recommendation. We address these issues in the present work by enriching the representation of scripted TV episodes through the use of an ontology of literary themes. The cold-start problem refers to the temporal situation where there is not enough information for an RS to produce new item or user recommendations. The user-cold-start scenario is commonly addressed by constructing a user profile by appealing to explicitly or implicitly provided user information (e.g., user preferences, demographic information, browsing history) [7; 8; 9]. In this paper, we address the item-cold-start scenario, where it is necessary to item metadata to associate it with other items that have already been seen by users. In contrast, the warm-system scenario is the ideal situation when both items and users are already known by the system, providing the conditions to perform a static evaluation. In our evaluations, we use item-cold-start (hereinafter cold-start) and warm-system settings to compare the performance of different recommendation algorithms.