Real-time electricity pricing and demand response has become a clean, reliable and cost-effective way of mitigating peak demand on the electricity grid. We consider the problem of end-user demand response (DR) for large commercial buildings which involves predicting the demand response baseline, evaluating fixed DR strategies and synthesizing DR control actions for load curtailment in return for a financial reward. Using historical data from the building, we build a family of regression trees and learn data-driven models for predicting the power consumption of the building in real-time. We present a method called DR-Advisor called DR-Advisor, which acts as a recommender system for the building's facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We evaluate the performance of DR-Advisor for demand response using data from a real office building and a virtual test-bed.