当前位置: X-MOL 学术arXiv.cs.AI › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Individualized Context-Aware Tensor Factorization for Online Games Predictions
arXiv - CS - Artificial Intelligence Pub Date : 2021-02-22 , DOI: arxiv-2102.11352
Julie Jiang, Kristina Lerman, Emilio Ferrara

Individual behavior and decisions are substantially influenced by their contexts, such as location, environment, and time. Changes along these dimensions can be readily observed in Multiplayer Online Battle Arena games (MOBA), where players face different in-game settings for each match and are subject to frequent game patches. Existing methods utilizing contextual information generalize the effect of a context over the entire population, but contextual information tailored to each individual can be more effective. To achieve this, we present the Neural Individualized Context-aware Embeddings (NICE) model for predicting user performance and game outcomes. Our proposed method identifies individual behavioral differences in different contexts by learning latent representations of users and contexts through non-negative tensor factorization. Using a dataset from the MOBA game League of Legends, we demonstrate that our model substantially improves the prediction of winning outcome, individual user performance, and user engagement.

中文翻译:

在线游戏预测的个性化上下文感知张量分解

个体行为和决策在很大程度上受其上下文(例如位置,环境和时间)影响。在多人在线战斗竞技场游戏(MOBA)中,可以很容易地观察到这些尺寸上的变化,在该游戏中,玩家每次比赛面对的游戏设置都不同,并且经常需要打补丁。现有的利用上下文信息的方法可以将上下文的影响推广到整个人群中,但是为每个人量身定制的上下文信息可能更有效。为了实现这一目标,我们提出了神经个性化上下文感知嵌入(NICE)模型,用于预测用户性能和游戏结果。我们提出的方法通过非负张量因子分解学习用户和上下文的潜在表示,从而识别不同上下文中的个体行为差异。
更新日期:2021-02-24
down
wechat
bug