Xie Xiaoxia, Jia Yuan, Ma Tiande
BYD Company Limited, Shenzhen, China.
School of Statistics, Renmin University of China, Beijing, China.
Front Neurorobot. 2024 Dec 4;18:1481297. doi: 10.3389/fnbot.2024.1481297. eCollection 2024.
The user perception of mobile game is crucial for improving user experience and thus enhancing game profitability. The sparse data captured in the game can lead to sporadic performance of the model. This paper proposes a new method, the balanced graph factorization machine (BGFM), based on existing algorithms, considering the data imbalance and important high-dimensional features. The data categories are first balanced by Borderline-SMOTE oversampling, and then features are represented naturally in a graph-structured way. The highlight is that the BGFM contains interaction mechanisms for aggregating beneficial features. The results are represented as edges in the graph. Next, BGFM combines factorization machine (FM) and graph neural network strategies to concatenate any sequential feature interactions of features in the graph with an attention mechanism that assigns inter-feature weights. Experiments were conducted on the collected game perception dataset. The performance of proposed BGFM was compared with eight state-of-the-art models, significantly surpassing all of them by AUC, precision, recall, and F-measure indices.
用户对手机游戏的感知对于改善用户体验进而提高游戏盈利能力至关重要。游戏中捕获的稀疏数据可能导致模型的零散性能。本文基于现有算法，考虑数据不平衡和重要的高维特征，提出了一种新方法——平衡图分解机（BGFM）。首先通过边界合成少数类过采样（Borderline-SMOTE oversampling）平衡数据类别，然后以图结构的方式自然地表示特征。其亮点在于BGFM包含用于聚合有益特征的交互机制。结果以图中的边表示。接下来，BGFM结合分解机（FM）和图神经网络策略，通过分配特征间权重的注意力机制连接图中特征的任何顺序特征交互。在收集的游戏感知数据集上进行了实验。将所提出的BGFM的性能与八个最先进的模型进行了比较，在AUC、精确率、召回率和F值指标上显著超过了所有这些模型。
