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  • Open Access


    Multi-Task Deep Learning with Task Attention for Post-Click Conversion Rate Prediction

    Hongxin Luo, Xiaobing Zhou*, Haiyan Ding, Liqing Wang

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3583-3593, 2023, DOI:10.32604/iasc.2023.036622

    Abstract Online advertising has gained much attention on various platforms as a hugely lucrative market. In promoting content and advertisements in real life, the acquisition of user target actions is usually a multi-step process, such as impression→click→conversion, which means the process from the delivery of the recommended item to the user’s click to the final conversion. Due to data sparsity or sample selection bias, it is difficult for the trained model to achieve the business goal of the target campaign. Multi-task learning, a classical solution to this problem, aims to generalize better on the original task given several related tasks by… More >

  • Open Access


    User Behavior Path Analysis Based on Sales Data

    Wangdong Jiang, Dongling Zhang*, Yapeng Peng, Guang Sun, Ying Cao, Jing Li

    Journal of New Media, Vol.2, No.2, pp. 79-90, 2020, DOI:10.32604/jnm.2020.010088

    Abstract With the rapid development of science and technology and the increasing popularity of the Internet, the number of network users is gradually expanding, and the behavior of network users is becoming more and more complex. Users’ actual demand for resources on the network application platform is closely related to their historical behavior records. Therefore, it is very important to analyze the user behavior path conversion rate. Therefore, this paper analyses and studies user behavior path based on sales data. Through analyzing the user quality of the website as well as the user’s repurchase rate, repurchase rate and retention rate in… More >

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