Paper Info
Title
OptMatch: Optimized Matchmaking via Modeling the High-Order Interactions on the Arena
Abstract
Matchmaking is a core problem for the e-sports and online games, which determines the player satisfaction and further influences the life cycle of the gaming products. Most of matchmaking systems take the form of grouping the queuing players into two opposing teams by following certain rules. The design and implementation of matchmaking systems are usually product-specific and labor-intensive. This paper proposes a two-stage data-driven matchmaking framework (namely OptMatch), which is applicable to most of gaming products and has the minimal product knowledge required. OptMatch contains an offline learning stage and an online planning stage. The offline learning stage includes (1) relationship mining modules to learn the low-dimensional representations of individuals by capturing the high-order inter-personal interactions, and (2) a neural network to incorporate the team-up effect and predict the match outcomes. The online planning stage optimizes the gross player utilities (i.e., satisfaction) during the matchmaking process, by leveraging the learned representations and predictive model. Quantitative evaluations on four real-world datasets and an online experiment on Fever Basketball game are conducted to empirically demonstrate the effectiveness of OptMatch.
Year
DOI
Venue
2020
10.1145/3394486.3403279
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event CA USA July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7998-4
5
PageRank 
References 
Authors
0.50
20
8
Name
Order
Citations
PageRank
Linxia Gong171.57
Xiaochuan Feng250.50
Dezhi Ye350.83
Hao Li4173.82
Runze Wu5114.73
Jianrong Tao65111.96
Changjie Fan75721.37
Peng Cui82317110.00