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. Longitudinal individual 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 transformation and feature extraction strategy is developed to transform the heterogeneous high-dimensional multi-relational datasets into customer-centered high-order tensors and to extract 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. The subagging strategy is adopted to deal with large-scale imbalanced datasets. Computational experiments using a real-world microblog database were conducted to investigate the benefits of integrating diverse heterogeneous data. Computational results show that the improved H-MK-SVM using longitudinal individual behavioral data exhibits superior performance over some commonly used methods using aggregated behavioral data and the improved H-MK-SVM using engagement behavioral data performs better than using only the external and individual behavioral data.