Modelling the user's multiple behaviors is an essential part of modern e-commerce, whose widely adopted application is to jointly optimize click-through rate (CTR) and conversion rate (CVR) predictions. Most of existing methods overlook the effect of two key characteristics of the user's behaviors: for each item list, (i) contextual dependence refers to that the user's behaviors on any item are not purely determinated by the item itself but also are influenced by the user's previous behaviors (e.g., clicks, purchases) on other items in the same sequence; (ii) multiple time scales means that users are likely to click frequently but purchase periodically. To this end, we develop a new multi-scale user behavior network named Hierarchical rEcurrent Ranking On the Entire Space (HEROES) which incorporates the contextual information to estimate the user multiple behaviors in a multi-scale fashion. Concretely, we introduce a hierarchical framework, where the lower layer models the user's engagement behaviors while the upper layer estimates the user's satisfaction behaviors. The proposed architecture can automatically learn a suitable time scale for each layer to capture the dynamic user's behavioral patterns. Besides the architecture, we also introduce the Hawkes process to form a novel recurrent unit which can not only encode the items' features in the context but also formulate the excitation or discouragement from the user's previous behaviors. We further show that HEROES can be extended to build unbiased ranking systems through combinations with the survival analysis technique. Extensive experiments over three large-scale industrial datasets demonstrate the superiority of our model compared with the state-of-the-art methods.

翻译：模拟用户的多重行为是现代电子商务的一个基本部分,其广泛采用的应用是共同优化点击通速率(CTR)和转换率(CVR)预测。大多数现有方法忽略了用户行为的两个关键特点的影响:对于每个项目列表,(一) 环境依赖是指用户对任何项目的行为并非纯粹被项目本身所确定,而且受用户以前在同一序列中对其他项目的行为(例如点击、购买)的影响; (二) 多重时间尺度意味着用户可能经常点击但定期购买。为此,我们开发了名为“Erarranchic ration Ranging Ranging On the Entire space (HEROES) 的新的多尺度用户行为网络,它包含背景信息,用以以多尺度方式估计用户的多重行为。具体地说,我们引入了一个等级框架,低层次模型来模拟用户的参与行为,而上层则进一步估算用户的满意度行为。拟议架构可以自动学习一个适合时间尺度的OO级行为网络,用以对比每个周期的系统结构,从而显示前层的顺序,我们只能通过新的系统来显示新的系统。