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dc.contributor.advisorOleksiy Khriyenko
dc.contributor.advisorVagan Terziyan
dc.contributor.authorOkojie, Charles
dc.date.accessioned2018-03-13T08:42:44Z
dc.date.available2018-03-13T08:42:44Z
dc.date.issued2018
dc.identifier.otheroai:jykdok.linneanet.fi:1860933
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57311
dc.description.abstractThe surge in the amount of information available on the internet and the number of users utilizing such information poses new challenges for information systems. This rapid growth in information is palpable in the news provisioning domain where users spend time deciding which of the many channels provides news in a most reliable and useful fashion. In the last few years, News aggregation platforms are now present which reduces the time users spends in consuming news from multiple sources. But news provisioning is more than just aggregating and presenting news. It must also include taking into cognizance users’ needs. Therefore, recommendation algorithms are developed by information systems and news contents are recommended and personalized for users while utilizing user’s specific data. If content recommendation is to be optimal, better and more efficient algorithms must be developed and implemented. Challenges associated with this type information systems include providing quality and novel information, feeding out relevant information while dealing with problems such as ‘data-sparsity’ commonly associated with recommending content. To this end I conduct a study which employs a hybrid approach to solving the existing problems with recommendation system and providing quality, relevant and serendipitous news content for users.en
dc.format.extent1 verkkoaineisto (83 sivua)
dc.language.isoeng
dc.rightsJulkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.fi
dc.rightsThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.en
dc.subject.otherIBM Watson
dc.subject.otherInformation services
dc.subject.otherWeb Scraping
dc.titleEnhancing news recommendation using a personalized content manager
dc.identifier.urnURN:NBN:fi:jyu-201803131716
dc.type.ontasotPro gradufi
dc.type.ontasotMaster's thesisen
dc.contributor.tiedekuntaInformaatioteknologian tiedekuntafi
dc.contributor.tiedekuntaFaculty of Information Technologyen
dc.contributor.laitosInformation Technologyen
dc.contributor.laitosInformaatioteknologia
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.date.updated2018-03-13T08:42:44Z
dc.rights.accesslevelopenAccessfi
dc.contributor.oppiainekoodi602
dc.subject.ysouutiset
dc.subject.ysosuosittelujärjestelmät
dc.subject.ysopersonointi
dc.subject.ysotietojärjestelmät


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