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A Hybrid Approach for Personalized News Recommendation in a Mobility Scenario Using Long-Short User Interest

dc.contributor.authorViana, Paula
dc.contributor.authorSoares, Márcio
dc.date.accessioned2018-01-16T10:56:26Z
dc.date.available2018-01-16T10:56:26Z
dc.date.issued2017
dc.description.abstractAccess to information has been made easier in different domains that range from multimedia content, books, music, news, etc. To deal with the huge amount of alternatives, recommendation systems have been often used as a solution to filter the options and provide suggestions of items that might be of interest to an user. The news domain introduces additional challenges due not only to the large amount of new items produced daily but also due to their ephemeral timelife. In this paper, a news recommendation system which combines content-based and georeferenced techniques in a mobility scenario, is proposed. Taking into account the volatility of the information, short-term and long-term user profiles are considered and implicitly built. Besides tracking users’ clicks, the system infers different levels of interest an article has by tracking and weighting each action in the system and in social networks. Impact of the different fields that make up a news is also taken into account by following the inverted pyramid model that assumes different levels of importance to each paragraph of the article. The solution was tested with a population of volunteers and results indicate that the quality of the recommendation approach is acknowledged by the users.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1142/S0218213017600120pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/10778
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWorld Scientific Publishingpt_PT
dc.subjectNews recommendation systempt_PT
dc.subjectGeolocationpt_PT
dc.subjectLong-short preferencespt_PT
dc.subjectHybrid recommenderpt_PT
dc.titleA Hybrid Approach for Personalized News Recommendation in a Mobility Scenario Using Long-Short User Interestpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue02pt_PT
oaire.citation.startPage1760012pt_PT
oaire.citation.titleInternational Journal on Artificial Intelligence Toolspt_PT
oaire.citation.volume26pt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT

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