Demo Data from VLDB 2026 / reviewers in the wild / expert
Shindong Kang
dblp:245/4441
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Recommender systems
user modeling |
0.6 | 1 | 2022 | UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI · AAAI 2022 |
Games and playful interaction
game AI |
0.6 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AIabstractThis 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 |
AAAI | 3 |