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Demo Data from VLDB 2026 / reviewers in the wild / expert

Youngbak Jo

dblp:245/4154 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Recommender systems · 100%
Human-computer interaction and pervasive computing
1 paper
Games and playful interaction · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems
user modeling		 0.612022
UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI · AAAI 2022
Games and playful interaction
game AI		 0.612022
UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI · AAAI 2022

Methods — techniques the papers use, named apart from their topics

style vector encoding · 1.1deep neural network · 1.1
YearPublicationVenuePosition
2022 UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI
abstract
This paper tries to resolve long waiting time to find a matching person in player versus player mode of online sports games, such as baseball, soccer and basketball. In player versus player mode, game playing AI which is instead of player needs to be not just smart as human but also show variety to improve user experience against AI. Therefore a need to design game playing AI agents with diverse personalized styles rises. To this end, we propose a personalized game AI which encodes user style vectors and card style vectors with a general DNN, named UCSM-DNN. Extensive experiments show that UCSM-DNN shows improved performance in terms of personalized styles, which enrich user experiences. UCSM-DNN has already been integrated into popular mobile baseball game: MaguMagu 2021 as personalized game AI.
Daegeun Choe, Youngbak Jo, Shindong Kang, Shounan An, Insoo Oh
 AAAI2