Exploring Personalizing Content Density Preference from User Behavior
Emil Sjölander · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2015
In an increasingly populated area of mobile applications craving a user’s attention it is of increasing importance to engage the user through a personalized user experience. This includes delivering content which the user is likely to enjoy as well as showcasing that content in a way such that the user is likely to interact with it. This thesis details the exploration of personalizing the content density of articles in the popular mobile application Flipboard. We use past user behavior to power classification of content density preference using random forests. Our results indicate that a personalized presentation of content does increase the overall engagement of Flipboard’s users however the error rate is current too high for the classifier to be usefu.l