Classic RSs are built on the assumption that the relevant data, e.g. ratings, contents and/or social relations, are independent and identical distributed (IID). Intuitively, this is inconsistent with real-life data characteristics, and cannot represent the heterogeneity and coupling relationships over relevant data. Therefore, we employ modern machine learning approaches to enhance RSs with complementary, comprehensive, and contextual (3C) information by coupling relevant heterogeneous data. This tutorial will analyze data, challenges, and business needs in advanced recommendation problems, and take non-IID perspective to introduce recent advances in machine learning to model the 3C-based next-generation RSs. This includes an overall of RS evolution and non-IIDness in recommendation, advanced machine learning for cross-domain RS, social RS, multimodal RS, multi-criteria RS, context-aware RS, and group-based RS, and their integration in building real-life RS.