Abstract
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.
Highlights
Multiple studies have been done in order to explore how to predict age, gender, authorship, region, and other characteristics of the author of a text
We discuss the results of the methodology that is described in Section 2.5 for text analysis with the PAN 2015 database DBT
Social networks like Twitter or Pinterest are essentially used for different purposes
Summary
Multiple studies have been done in order to explore how to predict age, gender, authorship, region, and other characteristics of the author of a text. Several statistics, people similarities, and trends are computed from the user activity in social networks, including the author profiling, which remains as an open multifactor challenge In this context, the most prominent conference about author profiling (PAN at CLEF) has been studying, since 2013, these topics; it was until 2018 when images were included as part of the training sets to explore the age and gender identification using images and text information. This work focuses on the analysis of users’ publications in social networks, Pinterest and Twitter, with the aim of establishing a representative user model based on age and gender. We evaluate two different types of datasets (from Twitter and Pinterest) to compare text and image features performance separately for predicting an initial profile for each user in the dataset (age and gender). These rules are huge number of images different and categories setthe upassociation the association rules These rules generated by the Apriori algorithm applied to recommend additional categories to Pinterest users.
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