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dc.contributor.advisorKuhail, Mohammad Amin
dc.contributor.authorGurram, Nikhil Sai Santosh
dc.date.issued2019
dc.date.submitted2019 Fall
dc.descriptionTitle from PDF of title page viewed June 2, 2020
dc.descriptionThesis advisor: Mohammad Amin Kuhail
dc.descriptionVita
dc.descriptionIncludes bibliographical references (pages 25-26)
dc.descriptionThesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019
dc.description.abstractTime is a constant entity and an invaluable element for every living person on this planet. Even with all modern-day technologies being available, many individuals like working professionals, students, and house makers often find a lack of time and time management as problems for successful task accomplishment. Many people face challenges in allocating time for their day to day work and personal life activities. One of the key reasons for this failure in task accomplishment is inefficient planning strategies for day to day tasks. There are many task management and to-do-list applications which focus on registering, organizing, sharing, and visualizing tasks, but most of them do not advise on optimal task management and recommendations for better performance. This problem has driven us to contribute a task recommender system which suggests a specific type of tasks to users based on their history of tasks and various factors at that specific time. This system not only suggests a specific type of task for the user but also collects feedback from the user to make the recommender system learn on how to provide useful recommendations thus making the users time much productive. For this system, we have taken some factors into consideration such as Day of the week, Time of the day, Type of the task, Weather, Location and Task completion success percentage. We have designed a rank score algorithm by drilling down to relevant data and by calculating Phi -Correlation on Task completion success percentage. This algorithm is used to provide recommendations for users for optimal task performance.eng
dc.description.tableofcontentsIntroduction -- Background and related work -- TaskDo - A daily task recommender system -- Evaluation -- Conclusion and future work
dc.identifier.urihttps://hdl.handle.net/10355/70783
dc.publisherUniversity of Missouri -- Kansas Cityeng
dc.subject.lcshRecommender systems (Information filtering)
dc.subject.otherThesis -- University of Missouri--Kansas City -- Computer science
dc.titleTaskDo: A Daily Task Recommender Systemeng
dc.typeThesiseng
thesis.degree.disciplineComputer Science (UMKC)
thesis.degree.grantorUniversity of Missouri--Kansas City
thesis.degree.levelMasters
thesis.degree.nameM.S. (Master of Science)


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