论文标题
预测在线项目选择行为：形状限制的回归观点
Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective
论文作者
论文摘要
本文研究了用户页面浏览量（PV）历史与其项目选择行为之间的关系。我们专注于PV序列，该序列代表了每个用户 - 项目对的PV数量的时间序列。我们提出了一个形状限制的优化模型，该模型可以准确估计所有可能的PV序列的项目选择概率。该模型根据用户以前的PVS的重新率和频率来利用PV序列的部分订单，对项目选择概率施加单调性约束。为了提高优化模型的计算效率，我们设计了有效的算法，以根据部分订单的传递性消除所有冗余约束。使用现实世界单击数据的实验结果表明，与最先进的优化模型和常见的机器学习方法相比，我们的方法实现了更高的预测性能。
This paper examines the relationship between user pageview (PV) histories and their item-choice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user--item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user's previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods.
