A recommender system based on implicit feedback for selective dissemination of ebooks
作者： Edward Rolando Núñez-Valdez , David Quintana , Ruben González Crespo , Pedro Isasi , Enrique Herrera-Viedma
DOI: 10.1016/J.INS.2018.07.068
关键词:
摘要: Abstract In this study, we describe a recommendation system for electronic books. The approach is based on implicit feedback derived from user’s interaction with content. User’s behavior tracked through several indicators that are subsequently used to feed the engine. This component then provides an explicit rating material interacted with. role of engine could be modeled as regression task where content rated according mentioned indicators. context, benchmark twelve popular machine learning algorithms perform final function and evaluate quality output provided by system.
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