Cognitive customer profiling at the edge: Challenges and research directions

The proliferation of automated services and applications has generated large amounts of data that must be handled with artificial intelligence and machine learning techniques, impacting businesses in different fields. Cognitive customer profiling services allow a better user experience enabling the tailoring of the customer experience to specific consumer patterns and preferences. Cognitive customer profiling services require heavy processing power in order to get accurate results from the artificial intelligence/machine learning models, which has led to services taking advantage of the cloud-to-edge continuum. This trending approach brings a set of challenges that have to be addressed. The more recent edge intelligence paradigm introduces artificial intelligence capacities to edge nodes, enabling a new set of solutions. However, artificial intelligence techniques must be adapted to deal with the specific characteristics of these scenarios. This paper identifies the challenges of running cognitive customer profiling in centralized scenarios, such as the cloud, to later introduce other approaches more suited for scenarios like the edge, identifying challenges and future research directions in the area.