arXiv cs.AI· Tobias Schneider, Lorenz Sparrenberg, Priya Tomar, Shahzeb Qamar, Tobias Deu{\ss}er, Rafet Sifa·· 3 小时前AI 评分21
PlayerEmbed：从原始输入序列学习玩家行为指纹表示
PlayerEmbed: Learning Behavioral Fingerprint Representations from Raw Input Sequences
AI 导读
PlayerEmbed 是一个基于深度度量学习的框架，用混合 CNN-LSTM 编码器从 1,000 名玩家的原始输入序列中学习紧凑且具区分性的玩家嵌入。在训练中见过的玩家和 1,000 名完全未见的玩家上评估，最近质心分类器的平衡准确率分别达 0.950 和 0.879。
正文 · 原文
Abstract:Raw player input sequences in video games contain fine-grained behavior, but are high-dimensional and noisy, which makes large-scale analytics challenging. To address this, we introduce PlayerEmbed, a framework that learns compact and discriminative player embeddings using deep metric learning. Our approach uses a hybrid CNN-LSTM encoder trained on raw input sequences from 1,000 players. We evaluate the model on held-out sequences from these players and on sequences from 1,000 players entirely unseen during training to assess generalization to unseen identities. For player identification, a nearest-centroid classifier built from reference embeddings achieves strong performance, reaching balanced accuracies of 0.950 for players seen during training and 0.879 for players unseen during training. Systematically increasing the number of training players substantially improves generalization to unseen players, indicating a transition from simple fingerprinting to learning transferable behavioral patterns.
| Comments: | Accepted at IEEE DSAA 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32727 [cs.LG] |
| (or arXiv:2610.32727v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32727 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tobias Schneider [view email]
[v1]
Fri, 23 Oct 2026 14:48:02 UTC (280 KB)
来源：arXiv cs.AI · arxiv.org