AAAI-2021 Tutorial: Towards Ubiquitous Recommender Systems: Data, Approaches, and Applications

Speakers: Dr Shoujin Wang, Dr Liang Hu

Authors: Dr Shoujin Wang, Dr Liang Hu, Prof. Yan Wang, Prof. Longbing Cao, Prof. Quan Z. Sheng, and Prof. Mehmet A. Orgun

Time: Feb 3, 2021

Location: Virtual event

AAAI Website: https://aaai.org/Conferences/AAAI-21/aaai21tutorials/

Abstract: This tutorial presents representative and state-of-the-art theories and approaches to build recommender systems (RS) and the ubiquitous applications of RS in nearly every aspect of our daily life, including eating, dressing, housing and travelling. To be specific, we will first present the background and foundations of RS, followed by the illustration of representative and advanced machine learning approaches that can be used for building RS, including latent factor models, deep learning models, graph learning models and knowledge graph models. Finally, we will demonstrate the ubiquity of RS by introducing the typical real-world applications of RS in both traditional domains, such as e-commerce, social network, and emerging domains including fashion industry, financial industry, and healthcare industry. We will conclude the tutorial by outlining future research directions in this area. No specific prerequisite knowledge is required, but a rudimentary knowledge of RS and some machine learning basic (e.g., factorization machine, deep learning) will be helpful.

Key references:

[1]. A survey on session-based recommender systems, ACM Computing Surveys (CSUR). 2021. [arXiv, ACM journal version]

[2]. Sequential recommender systems: challenges, progress and prospects, IJCAI 2019. [arXiv, IJCAI version]

[3]. Graph learning based recommender systems: a review, IJCAI 2021. [arXiv]