Year of Graduation
Application Recommeder System Development
Applied Mathematics and Information Science
We spend whole days with our phones constantly using them for a great variety of tasks. Often it can be very time-consuming to find the exact application that you need at the moment. In this work we propose a machine learning based recommender system that is capable of predicting what mobile application a user is willing to launch at any given moment of time. We describe its internal structure and its design and development process and show that it outperforms the approach previously used in Yandex Launcher for this task.