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Open AccessArticle

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

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Tupac Bio, Inc., San Francisco, CA 94103, USA
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Independent Researcher, London, SE13 7NZ, UK
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Systems and Computer Engineering Department, Universidad Nacional de Colombia, Ciudad Universitaria bldg. 453, Bogotá, D.C. 11001, Colombia
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Insitituto Caro y Cuervo, Calle 10 # 4-69, Bogotá, D.C. 111711, Colombia
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(9), 182; https://doi.org/10.3390/fi11090182
Received: 8 July 2019 / Revised: 9 August 2019 / Accepted: 14 August 2019 / Published: 22 August 2019
(This article belongs to the Special Issue New Perspectives on Semantic Web Technologies and Applications)
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. View Full-Text
Keywords: knowledge-based recommender systems; knowledge representation; literary theme; ontological engineering; ontology population; ontology-based recommender systems; Star Trek knowledge-based recommender systems; knowledge representation; literary theme; ontological engineering; ontology population; ontology-based recommender systems; Star Trek
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Sheridan, P.; Onsjö, M.; Becerra, C.; Jimenez, S.; Dueñas, G. An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise. Future Internet 2019, 11, 182.

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