Ontology-based news recommendation

IJntema, Wouter; Goossen, Frank; Frăsincar, Flavius; Hogenboom, Frederik

doi:10.1145/1754239.1754257

Cited by 118 publications

(80 citation statements)

References 10 publications

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“…We believe that DIY-CDR shows a promising future direction in ontology-based recommender systems. Actually, recent publications, such as [27] and [20], already show a few signs of this trend.…”

Section: Related Workmentioning

confidence: 91%

DIY-CDR: an ontology-based, Do-It-Yourself component discoverer and recommender

Tang

Meersman

2011

Pers Ubiquit Comput

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The do-it-yourself (DIY) culture has been continuously articulated since mid-1920s. The DIY spirit gets gradually spread because people realize that DIY activities are a way to confirm their personal creativities, outsource results, and expand their social contacts. We are motivated to design and implement a computing environment to support users to DIY their personalized Internet-of-things (IoT) applications. Before a person starts his DIY processes, it is important for him to find proper components that meet his needs. This article records our recent results concerning how to automatically discover and recommend existing components to users. The controlled fully automated ontologyassisted matching strategy (C-FOAM) is a matching strategy that contains algorithms at three different levels-string, lexical, and conceptual (or graphical). In this paper, we extend C-FOAM and embed it in a component discovery and recommendation module, which is called do-it-yourself component discoverer and recommender (DIY-CDR). DIY-CDR also uses semantic decision tables (SDT) to gather user specific decision rules and set up parameters for C-FOAM.

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Section: Related Workmentioning

confidence: 91%

DIY-CDR: an ontology-based, Do-It-Yourself component discoverer and recommender

Tang

Meersman

2011

Pers Ubiquit Comput

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“…Expressive metadata also stimulates purely algorithmic compilation of personalized products (i.e. Liu et al 2007;Bouras and Tsogkas 2009;Schouten et al 2010;Ijntema et al 2010;Goosen et al 2011) by calculating similarities between media assets, and thus improve the relevance of automated filtering and recommendation services on top of legacy data. This allows new forms of knowledge discovery and delivery services that go beyond the established search and retrieval paradigms and provide the users with a richer interaction experience, often at cost of privacy intrusion and disclosure of personal information.…”

Section: Linked Data In the Content Value Chainmentioning

confidence: 99%

Semantic metadata in the publishing industry – technological achievements and economic implications

Pellegrini

2016

Electron Markets

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This paper discusses the adoption of new data management practices, known as Linked Data, by publishing companies from a strategic management perspective. It investigates the role of semantic metadata in the creation and exploitation of so called Linked Data ecosystems and discusses organizational effects on business and value creation processes stimulated by the technology. The theoretical assumptions are complemented by two case studies that provide insights how publishing companies use Linked Data technologies to diversify their business, strategically position themselves within emerging business ecosystems and adapt to the affordances of advanced data management practices. The analysis reveals that despite of differences in the adoption strategy both publishing companies have identified Linked Data technologies as the core of their innovation strategy having a profound impact on existing business practices and new strategies of value creation.

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“…In addition to this, there are no explicit relation between different terms [33] . For instance, it is difficult to determine the relation between Google and Yahoo!…”

Section: Concept Weightingmentioning

confidence: 99%

Personalized News Recommendation: A Review and an Experimental Investigation

Wang

Zhu

et al. 2011

J. Comput. Sci. Technol.

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Online news articles, as a new format of press releases, have sprung up on the Internet. With its convenience and recency, more and more people prefer to read news online instead of reading the paper-format press releases. However, a gigantic amount of news events might be released at a rate of hundreds, even thousands per hour. A challenging problem is how to efficiently select specific news articles from a large corpus of newly-published press releases to recommend to individual readers, where the selected news items should match the reader's reading preference as much as possible. This issue refers to personalized news recommendation. Recently, personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Existing personalized news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. A variety of techniques have been proposed to tackle personalized news recommendation, including content-based, collaborative filtering systems and hybrid versions of these two. In this paper, we provide a comprehensive investigation of existing personalized news recommenders. We discuss several essential issues underlying the problem of personalized news recommendation, and explore possible solutions for performance improvement. Further, we provide an empirical study on a collection of news articles obtained from various news websites, and evaluate the effect of different factors for personalized news recommendation. We hope our discussion and exploration would provide insights for researchers who are interested in personalized news recommendation.

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Ontology-based news recommendation

Cited by 118 publications

References 10 publications

DIY-CDR: an ontology-based, Do-It-Yourself component discoverer and recommender

DIY-CDR: an ontology-based, Do-It-Yourself component discoverer and recommender

Semantic metadata in the publishing industry – technological achievements and economic implications

Personalized News Recommendation: A Review and an Experimental Investigation

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