Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users

• Main Authors: Roberto Garcia-Guzman, Yair A. Andrade-Ambriz, Mario-Alberto Ibarra-Manzano, Sergio Ledesma, Juan Carlos Gomez, Dora-Luz Almanza-Ojeda
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Applied Sciences
• Subjects: gender and age prediction; convolutional neural networks; category suggestion; social networks
• Online Access: https://www.mdpi.com/2076-3417/10/17/5957

Category suggestions or recommendations for customers or users have become an essential feature for commerce or leisure websites. This is a growing topic that follows users’ activity in social networks generating a huge quantity of information about their interests, contacts, among many others. These data are usually collected to analyze people’s behavior, trends, and integrate a complete user profile. In this sense, we analyze a dataset collected from Pinterest to predict the gender and age by processing input images using a Convolutional Neural Network. Our method is based on the meaning of the image rather than the visual content. Additionally, we propose a heuristic-based approach for text analysis to predict users’ age and gender from Twitter. Both of the classifiers are based on text and images and they are compared with various similar approaches in the state of the art. Suggested categories are based on association rules conformed by the activity of thousands of users in order to estimate trends. Computer simulations showed that our approach can recommend interesting categories for a user analyzing his current interest and comparing this interest with similar users’ profiles or trends and, therefore, achieve an improved user profile. The proposed method is capable of predicting the user’s age with high accuracy, and at the same time, it is able to predict gender and category information from the user. The certainty that one or more suggested categories be interesting to people is higher for those users with a large number of publications.