Behavior-aware User Response Modeling in Social Media: Learning from Diverse Heterogeneous Data

With the rapid development of Web 2.0 applications, social media have increasingly become a major factor influencing the purchase decisions of customers. Massive user behavioral, i.e., longitudinal individual behavioral and engagement behavioral, data generated on social media sites post challenges to integrate diverse heterogeneous data to improve prediction performance in customer response modeling. In this study, a hierarchical ensemble learning framework is proposed for behavior-aware user response modeling using diverse heterogeneous data. In the framework, a general-purpose data preprocessing and transformation strategy is developed to transform the large-scale and multi-relational datasets into customer centered external and behavioral datasets and to extract prediction attributes. An improved hierarchical multiple kernel support vector machine (H-MK-SVM) is developed to integrate the external, tag and keyword, individual behavioral and engagement behavioral data for feature selection from multiple correlated attributes and for ensemble learning in user response modeling. Computational experiments using a real-world microblog database were conducted to investigate the benefits of integrating diverse heterogeneous data. Computational results show that the H-MK-SVM using longitudinal individual behavioral data exhibits superior performance over some commonly used methods using aggregated behavioral data and the H-MK-SVM using engagement behavioral data exhibits superior performance over the methods using only the external and individual behavioral data.