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Real-time user-click recognition based on spark streaming

机译:基于火花流的实时用户点击识别

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With the rapid development of internet, data produced by users tends to be larger. The web page structure is becoming more and more complex and contains more and more different types of embedded objects as well. How to identify the user click requests from a large number of data set, is the first step as well as one of the difficulty of analysis of user behavior. Based on the spark streaming algorithm and some other previous algorithms, this paper proposes a real-time user-click recognition algorithm. The experimental results show that our algorithm is with high accuracy and is suitable for large scale data processing.
机译:随着互联网的快速发展,用户产生的数据趋向于更大。网页结构变得越来越复杂,并且还包含越来越多不同类型的嵌入式对象。如何从大量数据集中识别用户点击请求是第一步,也是分析用户行为的难点之一。基于火花流算法和其他一些先前的算法,本文提出了一种实时的用户点击识别算法。实验结果表明,该算法具有较高的精度,适用于大规模数据处理。

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