Authors: Jung-Min Kim; Hee-Deok Yang; Hyun-Sook Chung
Addresses: Department of Computer Engineering, Daejin University, Hogookro, Pocheon-si, Gyeonggi-do 487711, South Korea ' Department of Computer Engineering, Chosun University, Pilmoondaero, Nam-gu, Gwangju 501759, South Korea ' Department of Computer Engineering, Chosun University, Pilmoondaero, Nam-gu, Gwangju 501759, South Korea
Abstract: Many researches endeavour to solve the problem of information overload and deliver the preferred TV programme content to users. However, they have a weakness in the similarity computation of TV programmes for recommendation because they do not consider the knowledge structures of TV programme content. In this paper, we propose a similarity matching and recommendation method for TV programme content to reduce information overload. Our approach is composed of the three major tasks: (1) conceptualisation and construction of TV programme domain ontologies, (2) computation of the similarity of programme content based on domain ontologies and (3) user preference-based filtering and semantic score-based ranking of the recommendation list. To support semantic-based TV content searching and recommendation, we first design the semantic model of TV ontology and a conceptual process to transform the textual content descriptions of TV programmes to semantic maps. Subjective experiments confirm that the proposed method is effective in semantic-based searching and recommendation.
Keywords: TV programme ontology; content-based filtering; TV content searching; TV recommendations; recommender systems; personalisation; smart TV; smart television; television programmes; user preferences; similarity matching; information overload; domain ontologies; semantic maps.
International Journal of Web and Grid Services, 2015 Vol.11 No.3, pp.283 - 302
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 03 Aug 2015 *