论文标题

F形单击模型以获取多块移动页面上的信息检索

An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages

论文作者

Fu, Lingyue, Lin, Jianghao, Liu, Weiwen, Tang, Ruiming, Zhang, Weinan, Zhang, Rui, Yu, Yong

论文摘要

为了根据用户的隐式交互反馈提供点击模拟或相关性估计,在近年来,单击模型进行了很多研究。大多数点击模型都集中在用户行为上,指向单个列表。但是,随着用户界面设计(UI)设计的开发,结果页面上显示的项目的布局往往是多块(即多列表)样式,而不是单个列表,这需要不同的假设来更准确地建模用户行为。存在桌面上下文中多块页面的单击模型,但是由于不同的互动方式,结果类型,尤其是多块演示样式,因此无法直接应用于移动方案。特别是,多块移动页面通常可以分解为基本垂直块和水平块的交织,从而导致通常的F形式。为了减轻桌面和移动上下文之间的差距,我们进行了一项用户注视研究,并确定用户的顺序浏览,block skip和f s skip和f s fl-phape页面上的比较模式。这些发现导致了新型的F形点击模型(FSCM)的设计,该模型是多块移动页面的一般解决方案。首先,我们为每个页面构造一个有向的无环图(DAG),每个项目都被视为顶点,每个边缘表示用户可能的检查流程。其次,我们建议分别对用户的顺序(顺序浏览,块跳过)和非序列(比较)行为提出DAG结构的GRU和比较模块。最后,我们将GRU状态和比较模式结合在一起,以执行用户点击预测。与基线模型相比,大型现实世界数据集上的实验验证了FSCM对用户行为预测的有效性。

To provide click simulation or relevance estimation based on users' implicit interaction feedback, click models have been much studied during recent years. Most click models focus on user behaviors towards a single list. However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i.e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately. There exist click models for multi-block pages in desktop contexts, but they cannot be directly applied to mobile scenarios due to different interaction manners, result types and especially multi-block presentation styles. In particular, multi-block mobile pages can normally be decomposed into interleavings of basic vertical blocks and horizontal blocks, thus resulting in typically F-shape forms. To mitigate gaps between desktop and mobile contexts for multi-block pages, we conduct a user eye-tracking study, and identify users' sequential browsing, block skip and comparison patterns on F-shape pages. These findings lead to the design of a novel F-shape Click Model (FSCM), which serves as a general solution to multi-block mobile pages. Firstly, we construct a directed acyclic graph (DAG) for each page, where each item is regarded as a vertex and each edge indicates the user's possible examination flow. Secondly, we propose DAG-structured GRUs and a comparison module to model users' sequential (sequential browsing, block skip) and non-sequential (comparison) behaviors respectively. Finally, we combine GRU states and comparison patterns to perform user click predictions. Experiments on a large-scale real-world dataset validate the effectiveness of FSCM on user behavior predictions compared with baseline models.

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