Next-generation recommender systems and their advanced applications

Shoujin Wang; Liang Hu; Yan Wang; Longbing Cao; Quan Z. Sheng; Mehmet A. Orgun

Next-generation recommender systems and their advanced applications

Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet A. Orgun

School of Computing

Research output: Contribution to conference › Other

Abstract

This tutorial presents state-of-the-art theories and approaches to equip the next-generation recommender systems (RS), i.e., the latest and most advanced RS, and their advanced applica- tions. First, we will present the background and foundations of RS, followed by the illustration of three typical theories and approaches for building various next-generation RS: (1) sequential or session-based RS, (2) graph learning based RS, and (3) interactive and conversational RS, together with their prototypes. Finally, we will demonstrate three representative emerging real-world applied cases of RS in fashion industry using artificial intelligence (FashionAI), financial industry using technology (FinTech), and healthcare.

Original language	English
Publication status	Published - Jan 2021
Event	29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan Duration: 7 Jan 2021 → 15 Jan 2021

Conference

Conference	29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Country/Territory	Japan
City	Yokohama
Period	7/01/21 → 15/01/21

Access to Document

https://sites.google.com/view/shoujinwanghome/home/talks/ijcai-pricai-2020-tutorial

Cite this

@conference{f18e5b920e0342dd8a2c57ff8d9dc516,

title = "Next-generation recommender systems and their advanced applications",

abstract = "This tutorial presents state-of-the-art theories and approaches to equip the next-generation recommender systems (RS), i.e., the latest and most advanced RS, and their advanced applica- tions. First, we will present the background and foundations of RS, followed by the illustration of three typical theories and approaches for building various next-generation RS: (1) sequential or session-based RS, (2) graph learning based RS, and (3) interactive and conversational RS, together with their prototypes. Finally, we will demonstrate three representative emerging real-world applied cases of RS in fashion industry using artificial intelligence (FashionAI), financial industry using technology (FinTech), and healthcare.",

author = "Shoujin Wang and Liang Hu and Yan Wang and Longbing Cao and Sheng, {Quan Z.} and Orgun, {Mehmet A.}",

year = "2021",

month = jan,

language = "English",

note = "29th International Joint Conference on Artificial Intelligence, IJCAI 2020 ; Conference date: 07-01-2021 Through 15-01-2021",

}

TY - CONF

T1 - Next-generation recommender systems and their advanced applications

AU - Wang, Shoujin

AU - Hu, Liang

AU - Wang, Yan

AU - Cao, Longbing

AU - Sheng, Quan Z.

AU - Orgun, Mehmet A.

PY - 2021/1

Y1 - 2021/1

N2 - This tutorial presents state-of-the-art theories and approaches to equip the next-generation recommender systems (RS), i.e., the latest and most advanced RS, and their advanced applica- tions. First, we will present the background and foundations of RS, followed by the illustration of three typical theories and approaches for building various next-generation RS: (1) sequential or session-based RS, (2) graph learning based RS, and (3) interactive and conversational RS, together with their prototypes. Finally, we will demonstrate three representative emerging real-world applied cases of RS in fashion industry using artificial intelligence (FashionAI), financial industry using technology (FinTech), and healthcare.

AB - This tutorial presents state-of-the-art theories and approaches to equip the next-generation recommender systems (RS), i.e., the latest and most advanced RS, and their advanced applica- tions. First, we will present the background and foundations of RS, followed by the illustration of three typical theories and approaches for building various next-generation RS: (1) sequential or session-based RS, (2) graph learning based RS, and (3) interactive and conversational RS, together with their prototypes. Finally, we will demonstrate three representative emerging real-world applied cases of RS in fashion industry using artificial intelligence (FashionAI), financial industry using technology (FinTech), and healthcare.

UR - https://ijcai20.org/tutorials/

M3 - Other

T2 - 29th International Joint Conference on Artificial Intelligence, IJCAI 2020

Y2 - 7 January 2021 through 15 January 2021

ER -

Next-generation recommender systems and their advanced applications

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this