  In this paper, we present a novel system named DeepVisInterests that performs
the users interests prediction task from social visual data based on a deep
neural approach for the ontology construction. A comprehensive statistical
study have been made to validate our DeepVisInterests system. The proposed
system is based on the construction of users interests ontology using a set of
deep visual features in order to learn the semantic representation for the
popular topics of interests defined by Facebook. In fact, DeepVisInterests
system addressed the problem of discovering the attributed interests (how the
user interest can be detected from her/his provided social images in OSN) and
analyzing the performance of the automatic prediction by a comparison with the
self-assessed topics of interests (topics of interests provided by user in a
proposed questionnaire) through our experiments applied on social images
database collected from 240 Facebook users. The qualitative and the
quantitative experimental study made in this paper, show that DeepVisInterests
ranks top the list of recent related works with an accuracy of 0.80.
