User preferences profiling based on user behaviors on Facebook page categories
Rachsuda Jiamthapthaksin, Than Htike Aung · 2017
User preference profiling is important in both social networking mining and recommender systems. Facebook provides information of page category over two hundred relating to user preferences, but the predefined categories may not fit application well. Explicitly mapping those categories to a desirable set of user preferences is a tedious task. This paper proposes an effective user profiling technique using features constructed from user behaviours on Facebook page in different categories. The models created from three well known classification algorithms: Naïve Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM) turn the raw data of user behaviours into a set of user preferences defined by application. The experiments performed on Facebook dataset show that the constructed features are implicitly reflecting user preferences and can be used to tailor the preferences as needed. Among the three algorithms SVM leverages classification performance the most with accuracy over seventy-two percent.