Personalized Recommendation System Reflecting User Preference with Context-Awareness for Mobile TV

Yong, Suk Jung; Lee, Hyung Do; Yoo, Han Ku; Youn, Hee Yong; Song, Ohyoung

doi:10.1109/ispaw.2011.70

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…In this paper, we presented a novel set of OBF methods aimed at mitigating the item cold-start problem in the domain of fiction content recommendation. Unlike most conventional approaches [2,13,15,16], which exploit light-weight ontological representations of users and items, we propose a scheme for factoring large taxonomic hierarchies of item features directly into the recommendation process. In a case study, we compared the performance of our proposed OBF methods against a variety of alternatives in a Star Trek television series episode user rating prediction exercise.…”

Section: Discussionmentioning

confidence: 99%

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

Sheridan¹,

Onsjö

Becerra

et al. 2019

Future Internet

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Collaborative filtering based recommender systems have been 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 when items mostly belong to the same category and are mostly alike (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 algorithm. As 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 observed that our proposed method showed 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.

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Section: Discussionmentioning

confidence: 99%

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

Sheridan¹,

Onsjö

Becerra

et al. 2019

Future Internet

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“…Mobile: As the mobile market is growing, recent recommender systems also have used mobile devices to show the recommendation output (Hussein, 2009). In our classification of "Mobile", the applications of RSs installed into mobile devices (smartphones, tablets, PDAs, among others) are considered (Pessemier et al, 2008) as well as the Mobile TV applications (Yong et al, 2011). 3.…”

Section: Output Device Of Recommendationsmentioning

confidence: 99%

A literature review of recommender systems in the television domain

Véras

Prota

Bispo

et al. 2015

Expert Systems with Applications

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“…Yong et al [1] proposed a personalized recommendation scheme which considers the activities of the user at runtime and the information on the environment around the user. It allows efficient operation in mobile devices, and interoperability between the TV multimedia metadata and ontology.…”

Section: ) Ubiquitymentioning

confidence: 99%

“…It is however not easy for users to search or manage large volumes of multimedia contents in a mobile device with limited resources such as storage. To corroborate the importance of recommender systems in smart communities so that the aforementioned problems and challenges in mobile multimedia can be solved, some researchers [1], [7]- [20] have investigated and developed mobile multimedia recommender systems in different smart communities.…”

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confidence: 99%