Orchestration of campaigns for online display advertising requires marketers to forecast audience size at the granularity of specific attributes of web traffic, characterized by the categorical nature of all attributes (e.g. {US, Chrome, Mobile}). With each attribute taking many values, the very large attribute combination set makes estimating audience size for any specific attribute combination challenging. We modify Eclat, a frequent itemset mining (FIM) algorithm, to accommodate categorical variables. For consequent frequent and infrequent itemsets, we then provide forecasts using time series analysis with conditional probabilities to aid approximation. An extensive simulation, based on typical characteristics of audience data, is built to stress test our modified-FIM approach. In two real datasets, comparison with baselines including neural network models, shows that our method lowers computation time of FIM for categorical data. On hold out samples we show that the proposed forecasting method outperforms these baselines.

该研究通过修改经典的频繁项集挖掘算法Eclat，并运用时序分析方法提供条件概率的预测，来应对在线广告投放中基于网站属性的受众规模预测难题，并通过广泛的模拟验证和真实数据集验证，证明了该算法在缩短计算时间和提升预测精度方面的优势。

在线广告精准受众规模预测