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dc.contributor.authorFernández-García, Antonio Jesús
dc.contributor.authorRodriguez-Echeverría, Roberto
dc.contributor.authorPreciado, Juan Carlos
dc.contributor.authorPerianez, Jorge
dc.contributor.authorGutiérrez, Juan D.
dc.date2022
dc.date.accessioned2022-12-07T12:56:01Z
dc.date.available2022-12-07T12:56:01Z
dc.identifier.issn09574174
dc.identifier.urihttps://reunir.unir.net/handle/123456789/13873
dc.description.abstractThe rise of Recommender Systems has made their presence very common today in many domains. An example is the domain of radio or TV broadcasting content recommendations. The approach proposed here allows radio listeners to receive customized recommendations of radio channels they might listen to based on their specific preferences and/or historical data. Firstly, a Data Acquisition System is presented with its main task being to obtain and process data to pass to recommenders. Secondly, a dynamic hybrid Recommender System is developed based on four dimensions reflecting major aspects of radio programs: relative talk/music percentages, music genres, topics covered, and speech tone. Eight recommenders are constructed (two per dimension) using content-based or collaborative filtering algorithms depending on the nature of the data processed, whether historical data or user preferences. And thirdly, by assigning weights in accordance with the users’ preferences, a dynamic ensemble of these recommenders is formed which produces the final recommendations. Experiments were carried out illustrating the usefulness of the recommendations and its acceptance by radio listeners.es_ES
dc.language.isoenges_ES
dc.publisherExpert Systems with Applicationses_ES
dc.relation.ispartofseries;vol. 198, nº 116706
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0957417422001841?via%3Dihubes_ES
dc.rightsopenAccesses_ES
dc.subjectcontent-basedes_ES
dc.subjectensemble of recommenderses_ES
dc.subjecthybrid recommenderes_ES
dc.subjectradio programses_ES
dc.subjectrecommender systemes_ES
dc.subjectScopuses_ES
dc.subjectJCRes_ES
dc.titleA hybrid multidimensional Recommender System for radio programses_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2022.116706


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