Semisupervised Game Player Categorization From Very Big Behavior Log Data
提出一种并行半监督框架，利用少量已知标签的诱饵玩家，从超大规模MMORPG日志数据中高效识别特定玩家类别（如恶意机器人），并通过层次化并行计算解决计算复杂度挑战。
Extracting the specific category of the players, such as the malignant Bot, from the huge log data of the massive multiplayer online role playing games, denoted as MMORPGs, is an important basic task in game security and personal recommendation. In this article, we propose a parallel semisupervised framework to categorize specific game players with a few label-known target samples, which are denoted as bait players. Our approach first presents a feature representation model based on the players’ level granularity, which can acquire aligned feature representations in the lower dimensional space from the players’ original action sequences. Then, we propose a semisupervised clustering method, extended from the bisecting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means model, to extract the specified players with the help of those bait players. Due to massive amounts of game log data, the computation complexity is an extreme challenge to implement our feature representation and semisupervised extraction approaches. We also propose a hierarchical parallelism framework, which allows the data to be computed horizontally and vertically simultaneously and enables varied parallel combinations for the steps of our semisupervised categorization approach. The comparable experiments on real-world MMORPGs’ log data, containing more than 465 Gbytes and million players, are carried out to demonstrate the effectiveness and efficiency of our proposed approach compared with the state-of-the-art methods.