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Collaborative filtering based recommender systems have proven to be extremelysuccessful in settings where user preference data on items is abundant.However, collaborative filtering algorithms are hindered by their weaknessagainst the item cold-start problem and general lack of interpretability.Ontology-based recommender systems exploit hierarchical organizations of usersand items to enhance browsing, recommendation, and profile construction. Whileontology-based approaches address the shortcomings of their collaborativefiltering counterparts, ontological organizations of items can be difficult toobtain for items that mostly belong to the same category (e.g., televisionseries episodes). In this paper, we present an ontology-based recommendersystem that integrates the knowledge represented in a large ontology ofliterary themes to produce fiction content recommendations. The main novelty ofthis work is an ontology-based method for computing similarities between itemsand its integration with the classical Item-KNN (K-nearest neighbors)algorithm. As a study case, we evaluated the proposed method against otherapproaches by performing the classical rating prediction task on a collectionof Star Trek television series episodes in an item cold-start scenario. Thistransverse evaluation provides insights into the utility of differentinformation resources and methods for the initial stages of recommender systemdevelopment. We found our proposed method to be a convenient alternative tocollaborative filtering approaches for collections of mostly similar items,particularly when other content-based approaches are not applicable orotherwise unavailable. Aside from the new methods, this paper contributes atestbed for future research and an online framework to collaboratively extendthe ontology of literary themes to cover other narrative content.

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise