个性化情境感知张量分解在网络游戏预测中的应用
Individualized Context-Aware Tensor Factorization for Online Games Predictions
摘要：
个人的行为和决定受到其背景的影响，如位置，环境和时间。在多人在线对战竞技场游戏（MOBA）中可以很容易地观察到这些维度的变化，其中玩家在每场比赛中都面临不同的游戏设置，并且经常受到游戏补丁的影响。利用上下文信息的现有方法概括了上下文对整个人群的影响，但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，用于预测用户的表现和游戏结果。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来识别不同上下文中的个体行为差异。使用来自MOBA游戏League of Legends的数据集，我们证明了我们的模型大大提高了对获胜结果，个人用户表现和用户参与度的预测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
