首页|Data-driven human and bot recognition from web activity logs based on hybrid learning techniques
Data-driven human and bot recognition from web activity logs based on hybrid learning techniques
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Data-driven human and bot recognition from web activity logs based on hybrid learning techniques
Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performance metrics:although some users can be unambiguously classified as bots,the correct label is uncertain in many cases.This calls for the use of classifiers capable of explaining their decisions.This paper demonstrates two such mechanisms based on features carefully engineered from web logs.The first is a man-made rule-based system.The second is a hierarchical model that first performs clustering and next classification using human-centred,interpretable methods.The stability of the proposed methods is analyzed and a minimal set of features that convey the class-discriminating information is selected.The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.
Web logsClassificationClusteringWeb trafficBotsInterpretability
Marek Gajewski、Olgierd Hryniewicz、Agnieszka Jastrz?bska、Mariusz Kozakiewicz、Karol Opara、Jan Wojciech Owsiński、Slawomir Zadro?ny、Tomasz Zwierzchowski
展开 >
Systems Research Institute,Polish Academy of Sciences,Warsaw,Poland
Faculty of Mathematics and Information Science,Warsaw University of Technology,Warsaw,Poland
EDGE NPD Ltd.Co,Warsaw,Poland
Web logs Classification Clustering Web traffic Bots Interpretability
NETL
NSTL
万方数据
