  In this paper user modeling task is examined by processing a gallery of
photos and videos on a mobile device. We propose novel engine for user
preference prediction based on scene recognition, object detection and facial
analysis. At first, all faces in a gallery are clustered and all private photos
and videos with faces from large clusters are processed on the embedded system
in offline mode. Other photos may be sent to the remote server to be analyzed
by very deep models. The visual features of each photo are obtained from scene
recognition and object detection models. These features are aggregated into a
single user descriptor in the neural attention block. The proposed pipeline is
implemented for the Android mobile platform. Experimental results with a subset
of Photo Event Collection, Web Image Dataset for Event Recognition and Amazon
Fashion datasets demonstrate the possibility to process images very efficiently
without significant accuracy degradation. The source code of Android mobile
application is publicly available at
https://github.com/HSE-asavchenko/mobile-visual-preferences.
