重新定义点击模型设计基础,为新界面提供统一分析框架。
Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models
- 基于数学性质提出三项核心设计选择,解释模型能捕捉的统计模式。
- 构建首个涵盖传统、网格和轮播界面的统一分类体系,兼容概率图与神经网络模型。
- 为轮播界面设计新模型提供理论依据,适合点击行为研究者参考。
点击模型是建模用户与网页交互的成熟方法。以往研究主要聚焦于传统的单列表搜索场景,现有综述基于第一代概率图模型(PGM)进行分类,已难以应对新界面(如轮播界面)和神经网络(NN)模型的挑战。本文认为,旧有分类框架无法有效比较PGM与NN模型,也无法推广至新型界面,阻碍了新模型的发展。为此,本文从数学性质出发,重新界定点击模型设计的基本概念,提出三项核心设计选择,用以解释模型可捕捉的统计模式及潜在用户行为。基于此,构建首个涵盖单列表、网格和轮播界面的统一分类体系,首次实现对PGM与NN模型的有意义比较。最后,通过一个轮播界面新模型的推导,展示该理论框架对未来模型设计的支持能力。
原文摘要 · Abstract (English)
Click models are a well-established for modeling user interactions with web interfaces. Previous work has mainly focused on traditional single-list web search settings; this includes existing surveys that introduced categorizations based on the first generation of probabilistic graphical model (PGM) click models that have become standard. However, these categorizations have become outdated, as their conceptualizations are unable to meaningfully compare PGM with neural network (NN) click models nor generalize to newer interfaces, such as carousel interfaces. We argue that this outdated view fails to adequately explain the fundamentals of click model designs, thus hindering the development of novel click models. This work reconsiders what should be the fundamental concepts in click model design, grounding them - unlike previous approaches - in their mathematical properties. We propose three fundamental key-design choices that explain what statistical patterns a click model can capture, and thus indirectly, what user behaviors they can capture. Based on these choices, we create a novel click model taxonomy that allows a meaningful comparison of all existing click models; this is the first taxonomy of single-list, grid and carousel click models that includes PGMs and NNs. Finally, we show how our conceptualization provides a foundation for future click model design by an example derivation of a novel design for carousel interfaces.
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