Non-IID Recommender Systems in Practice with Modern AI Techniques


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The renaissance of artificial intelligence (AI) has attracted huge attention from every corner of the world. Specially, machine learning approaches have deeply involved in AI research in almost all areas, e.g., natural language processing (NLP), computer vision (CV). In particular, recommender systems (RS), as probably one of the most widely used AI systems, has integrated into every part of our daily life. In this AI age, state-of-the-art machine learning approaches, e.g. deep learning, have become the primary choice to model advanced RSs. 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 Comprehensive, Complementary, 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 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.