Abstract
Deep data analysis for latent information prediction has been an
important research area. Many of the existing solutions have used the
textual data and have obtained an accurate results for predicting users’
interests and other latent attributes. However, little attention has
been paid to visual data that is becoming increasingly popular in recent
times. In this paper, we addresses the problem of discovering the
attributed interest and of analyzing the performance of the automatic
prediction using a comparison with the self assessed topics of interest
(topics of interest provided by the user in a proposed questionnaire)
based on data analysis techniques applied on the users visual data. We
analyze the content of each user’s images to aggregate the image-level
users’ interests distribution in order to obtain the user-level users’
interest distribution. To do this, we employ the pretrained ImageNet
convolutional neural networks architectures for the feature extraction
step and to construct the ontology, as the users’ interests model, in
order to learn the semantic representation for the popular topics of
interests defined by social networks (e.g., Facebook). Our experimental
studies show that this analysis, on the most relevant features, enhances
the performance of the prediction framework. In order to improve our
framework’s robustness and generalization with unknown users’ profiles,
we propose a novel database evaluation. Our proposed framework provided
promising results which are competitive to state-of-the-art techniques
with an accuracy of 0.80.