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Deep Learning based Content Selection System

Year: 2017
Tags: Deep Learning


Automated content selection is a challenging due to the diversity of data and user preferences. The changing demands by user are mostly based on external conditions like weather, temperature and geographic terrains. These factors directly influence culture and hence preferences of people. The aim of this project is take into account the environmental conditions of the user and predict a food preference they would most likely be interested in. For example, cold beverages on a clear sunny day and a light meal during break hours. We used an open source data collection to train a neural network that meets the demand of the user by studying their environmental conditions. The choice of neural networks translates into the need for a predictor adoptable to changing human nature, using self-training abilities.