Deep approach based on user’s profile analysis for capturing user’s interests

Abstract

Capturing user’s interests and preferences by analyzing and interpreting the daily-shared contents in online social networks offer a unique information source for several domains such as business, marketing and politics. User’s profile describes its owner’s characteristics, where it contains several important personal information such as (age, sex, job title, level of education, etc.), which can help to improve the process of user’s interests identification. This information can typically represent a range of values representing only one user profile. Hence, the shared posts, the reactions on other posts and their circle of friends can help to reflect their interests. However, exploiting all this information through the analysis of user profiles can help to enhance user’s interests identification performances. In this paper, we propose a deep learning user’s profile analysis based approach that relies on users’ personal information and textual content for detecting user’s interests and preferences. We experimented our approach using a large Facebook dataset, and show how the deep learning approach perform significantly better than the classical algorithms such as SVM.