Semantics-Based Framework for Personalized Access to TV Content: The iFanzy Use Case

Bellekens, Pieter; Aroyo, Lora; Houben, Geert‐Jan; Kaptein, Annelies; Sluijs, Kees van der

doi:10.1007/978-3-540-76298-0_65

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Appendix B: List of the adaptive systems we considered in the survey ADAPT2 framework (Brusilovsky et al 2005) AutoTag (Mishne 2006) Bibster (Haase et al 2005) Carmagnola and Dimitrova (2008) Cena and Furnari (2008) CHIME framework (Chepegin et al 2003) Schwarzkopf et al (2007) SenSee framework and iFanzy application (Bellekens et al 2007) Smart Tag Recommender (Basile et al 2007) SmartWeb (Sonntag et al 2007) Shepitsen et al (2008) Szomszor et al (2008 TANGRAM (Jovanovic et al 2006) TBCF Trust-Aware Recommender (Ziegler and Lausen 2004) TrustProject (Golbeck and Mannes 2006) UbisWord/GUMO (Heckmann 2005) Wang et al (2008 Yahoo! MyWeb 2.0 (Xu et al 2006) …”

Section: Torrementioning

confidence: 99%

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Adaptive systems in the era of the semantic and social web, a survey

Torre

2009

User Model User-Adap Inter

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In this paper we provide a classification of adaptive systems with respect to the kind of semantic technology they exploit to accomplish or improve specific adaptation and user modeling tasks. This classification is based on a distinction between strong semantic techniques and weak semantic techniques. The former are techniques based on the Semantic Web, while the latter regard technologies that, in different ways, annotate resources, enriching their meaning. This second category includes, in particular, Web 2.0 social annotations and mixed approaches between social annotations and Semantic Web techniques. While the impact of the Semantic Web on adaptive systems has been discussed in several survey papers, the potential of weak semantic technologies has, so far, received little attention. The aim of this analysis is to fill this gap. Therefore, we will discuss contributions and limits of both approaches, but we will focus special attention on weak semantic adaptive systems.

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Section: Torrementioning

confidence: 99%

“…Other SASs use ad hoc transformations. iFanzy (Bellekens et al 2007), for example, integrates several sources transforming them into OWL/RDF and using SKOS to describe relations between concepts.…”

Section: Strong Semantic Techniquesmentioning

confidence: 99%

Adaptive systems in the era of the semantic and social web, a survey

Torre

2009

User Model User-Adap Inter

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“…they can search and browse the data based on facets for time, location and genre. People daily retrieve the metadata from the different multimedia sources on the Web [7].…”

Section: Semantics-based Framework For Personalized Access To Tv Cmentioning

confidence: 99%

Development of Semantic Model and Information Exploration System for TV Media

Çelik

Elçi

Calik

et al. 2012

2012 IEEE 36th Annual Computer Software and Applications Conference Workshops

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This study proposes an ontology-based system which takes the non-structured/semi-structured heterogeneous TV content in heterogeneous Web resources and converts it to the structured form in a semantic data model. The TV content is discovered and redefined after aligning in semantic base according to client requirements by a semantic search agent. Through a system that is intuitive and semantic, semantic based matchmaking is applied between the concepts of required TV content information and related heterogeneous Web resources. By this study, a search assistant system which makes semantic searching in TV Web content, will be designed to satisfy user needs. As a result, the system architecture is proposed based on the TV domain ontology base facilitating personalized access to heterogeneous TV contents of Web documents.

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“…We choose CLC as the skeleton of the domain ontology mainly for two reasons: i) CLC is a language-independent schema, so that we do not have to distinguish between books written in different languages, and ii) all the books in our experimental dataset are indexed by the CLC categories. The method that utilizes existing vocabularies to construct an ontology has been widely adopted by previous studies [2,7].…”

Section: Domain Ontologymentioning

confidence: 99%

Personalized search in digital libraries via spreading activation model

Sun

Zhang

Yan

et al. 2013

Web Intelligence and Agent Systems: An International Journal

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With the tremendous development of information technology, the volume of data in digital libraries is increasing enormously, and the magnitude is putting users at risk of information overload. Personalized search aims at solving the problem by tailoring search results for individual demands. We propose a novel approach to provide personalized search by learning users' interests from historic behaviors and re-ranking search results by a Spreading Activation (SA) model. In our approach, users' interests are categorized by the level of recency to form user profiles, which in turn serve as the input of the SA model. Then, SA runs on the domain ontology incorporated with a newly defined relationship borrowIntent derived from the assumption of collaborative filtering. We further demonstrate the effectiveness of the proposed methodology through experiments on real data from a university library. The presented approach can also be applied in other contexts such as electronic commerce.

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Semantics-Based Framework for Personalized Access to TV Content: The iFanzy Use Case

Cited by 7 publications

References 5 publications

Adaptive systems in the era of the semantic and social web, a survey

Adaptive systems in the era of the semantic and social web, a survey

Development of Semantic Model and Information Exploration System for TV Media

Personalized search in digital libraries via spreading activation model

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