TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning.
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| Title: | TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning. |
|---|---|
| Authors: | Narducci, Fedelucio1 fedelucio.narducci@uniba.it, Musto, Cataldo1 cataldo.musto@uniba.it, de Gemmis, Marco1 marco.degemmis@uniba.it, Lops, Pasquale1 pasquale.lops@uniba.it, Semeraro, Giovanni1 giovanni.semeraro@uniba.it |
| Source: | Information Systems Frontiers. Dec2018, Vol. 20 Issue 6, p1157-1171. 15p. 1 Color Photograph, 3 Diagrams, 8 Charts, 1 Graph. |
| Subjects: | Electronic program guides (Television), Television programs, Information retrieval, Machine learning, Information display systems |
| Abstract: | Electronic Program Guides (EPGs) are systems that allow users of media applications, such as web TVs, to navigate scheduling information about current and upcoming programming. Personalized EPGs help users to overcome information overload in this domain, by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this paper we introduce the concept of personal channel, on which Personalized EPGs are grounded, that provides users with potentially interesting programs and videos, by exploiting program genres (documentary, sports, ...) and short textual descriptions of programs to find and categorize them. We investigate the problem of adopting appropriate algorithms for TV-program classification and retrieval, in the context of building personal channels, which is harder than a classical retrieval or classification task because of the short text available. The approach proposed to overcome this problem is the adoption of a new feature generation technique that enriches the textual program descriptions with additional features extracted from Wikipedia. Results of the experiments show that our approach actually improves the retrieval performance, while a limited positive effect is observed on classification accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of Information Systems Frontiers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 133377810 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Narducci%2C+Fedelucio%22">Narducci, Fedelucio</searchLink><relatesTo>1</relatesTo><i> fedelucio.narducci@uniba.it</i><br /><searchLink fieldCode="AR" term="%22Musto%2C+Cataldo%22">Musto, Cataldo</searchLink><relatesTo>1</relatesTo><i> cataldo.musto@uniba.it</i><br /><searchLink fieldCode="AR" term="%22de+Gemmis%2C+Marco%22">de Gemmis, Marco</searchLink><relatesTo>1</relatesTo><i> marco.degemmis@uniba.it</i><br /><searchLink fieldCode="AR" term="%22Lops%2C+Pasquale%22">Lops, Pasquale</searchLink><relatesTo>1</relatesTo><i> pasquale.lops@uniba.it</i><br /><searchLink fieldCode="AR" term="%22Semeraro%2C+Giovanni%22">Semeraro, Giovanni</searchLink><relatesTo>1</relatesTo><i> giovanni.semeraro@uniba.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Information+Systems+Frontiers%22">Information Systems Frontiers</searchLink>. Dec2018, Vol. 20 Issue 6, p1157-1171. 15p. 1 Color Photograph, 3 Diagrams, 8 Charts, 1 Graph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electronic+program+guides+%28Television%29%22">Electronic program guides (Television)</searchLink><br /><searchLink fieldCode="DE" term="%22Television+programs%22">Television programs</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Information+display+systems%22">Information display systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Electronic Program Guides (EPGs) are systems that allow users of media applications, such as web TVs, to navigate scheduling information about current and upcoming programming. Personalized EPGs help users to overcome information overload in this domain, by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this paper we introduce the concept of personal channel, on which Personalized EPGs are grounded, that provides users with potentially interesting programs and videos, by exploiting program genres (documentary, sports, ...) and short textual descriptions of programs to find and categorize them. We investigate the problem of adopting appropriate algorithms for TV-program classification and retrieval, in the context of building personal channels, which is harder than a classical retrieval or classification task because of the short text available. The approach proposed to overcome this problem is the adoption of a new feature generation technique that enriches the textual program descriptions with additional features extracted from Wikipedia. Results of the experiments show that our approach actually improves the retrieval performance, while a limited positive effect is observed on classification accuracy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Information Systems Frontiers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=133377810 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10796-017-9780-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1157 Subjects: – SubjectFull: Electronic program guides (Television) Type: general – SubjectFull: Television programs Type: general – SubjectFull: Information retrieval Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Information display systems Type: general Titles: – TitleFull: TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Narducci, Fedelucio – PersonEntity: Name: NameFull: Musto, Cataldo – PersonEntity: Name: NameFull: de Gemmis, Marco – PersonEntity: Name: NameFull: Lops, Pasquale – PersonEntity: Name: NameFull: Semeraro, Giovanni IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 13873326 Numbering: – Type: volume Value: 20 – Type: issue Value: 6 Titles: – TitleFull: Information Systems Frontiers Type: main |
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