AAAI 2018 Tutorial

When Advanced Machine Learning Meets Intelligent Recommender Systems

Speakers: Liang Hu and Songlei Jian

Authors: Liang Hu, Songlei Jian, Prof. Longbing Cao and Prof. Jian Cao

External Authors: Shoujin Wang

Time: 2:00 – 6:00 PM, Saturday, Feb. 3rd

Goal of the Tutorial

Nowadays, 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) and game playing.

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. Current machine learning methods are built on data, therefore the recommendation tasks can be regarded as typical AI problems to learn and infer from data.

The goal of this tutorial aims to enable both academic and practical audience with a comprehensive understanding and relevant techniques of how to apply state-of-the-art machine learning approaches to build more sensible next-generation RSs in contexts with various heterogeneous data and complex relations. In this tutorial, we will present a systematic review and applications of recent advanced machine learning techniques to build real-life intelligent RSs. After this tutorial, the audience can walk away with:

  • The insight into recent development and evolution of recommendation techniques;
  • The machine learning methods to model complex couplings over heterogeneous recommendation data in a comprehensive way;
  • The various development of advanced RSs built on the state-of-the-art machine learning methods;
  • The practical approaches to customize and build advanced RSs over audience's own complex data with the ideas, models and techniques learned from this tutorial.


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.


slides are here: PDF

when advanced machine learning meets intelligent recommendersystems-latest.pdf


  • Books & Surveys
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    • Complementary Information in Recommender Systems
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    • Comprehensive Information in Recommender Systems
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    • Context Information in Recommender Systems
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    • Real-world Recommender Systems