Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1070
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dc.contributor.authorSilva, K. G. G. H.-
dc.contributor.authorAbeyasekare, W. A. P. S.-
dc.contributor.authorDasanayake, D.M. H. E.-
dc.contributor.authorNandisena, T. B.-
dc.contributor.authorKasthurirathna, D.-
dc.contributor.authorKugathasan, A.-
dc.date.accessioned2022-02-09T09:07:51Z-
dc.date.available2022-02-09T09:07:51Z-
dc.date.issued2021-12-09-
dc.identifier.issn978-1-6654-0862-2/21-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1070-
dc.description.abstractPersonalization is one of the most sought out and popular methods for brand recognition and consumer attraction. The usage of deep reinforcement learning due to its’ ability to learn actions the way humans learn from experience, if utilized and evaluated properly it can result in a revolutionary effect on personalization. The methodology proposed in this research utilizes deep reinforcement learning where an artificial agent may be trained by interacting with its environment. Utilizing the experience gathered, the agent is able optimize in the form of rewards. The approach explained, can be utilized across applications which can be personalized. Several scenarios ranging from changing the layout of webpages, to rearranging icons on mobile home screens are discussed. The main objective is to develop an API for the web developers and smartphone manufacturers to utilize so that depending on the application personalization can be achieved by enhancing saliency, minimizing selection time, increasing engagement, or an arrangement of these. The technique can manage a variety of adaptations, such as how graphical elements are shown and how they behave. An experiment was conducted which showcased improved user experience considering the position change of theen_US
dc.language.isoenen_US
dc.subjectPersonalizationen_US
dc.subjectDeep reinforcement learningen_US
dc.subjectDeep Q learningen_US
dc.subjectMarkov decision processen_US
dc.subjectUser experienceen_US
dc.titleDynamic User Interface Personalization Based on Deep Reinforcement Learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC54203.2021.9671076en_US
Appears in Collections:3rd International Conference on Advancements in Computing (ICAC) | 2021
Research Papers - Open Access Research

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