DeepVisInterests: CNN-Ontology Prediction of Users Interests from Social Images

Onsa Lazzez, Wael Ouarda, Adel M. Alimi

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.

Knowledge Graph

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