AI 中文总结
该研究针对在线产品搜索中产品外观相似的问题，提出上下文视觉区分度与相对适配度概念，经分析超80万次电商搜索事件，发现区分度溢价随适配度提升，可作为局部注意力分配机制。
AI 中文摘要
在在线产品搜索中，返回的备选产品往往外观相似。我们研究当某产品在返回集合中与其最相似的“孪生产品”的视觉区分度提升时，其被选择的概率会如何变化。我们引入两个场景层面的概念：上下文视觉区分度（即考虑集合中与最近的相似备选产品的图像距离）和相对适配度（即与搜索查询的匹配程度）。我们假设，区分度有利于产品被选中且具有高度上下文依赖性，会产生随相对适配度提升的溢价，且在文本描述无法区分选项时最为重要。我们使用密集表征分析了超过80万次电子商务搜索事件，应用搜索事件和产品固定效应来评估同一产品在不同视觉邻居下的表现。结果显示，当产品没有相近的“孪生产品”时，其被点击的概率显著更高。这种区分度溢价随相对适配度提升，且在竞争产品的描述高度相似时大致翻倍。这与“区分度有助于在预评估阶段脱颖而出而非提升固有效用”的模型一致，区分度带来的额外点击后续转化概率会低5%-7%。最终，这些证据表明视觉差异化是一种局部注意力分配机制，其价值取决于查询适配度和竞争线索的信息。
英文摘要
In online product search, returned alternatives often look alike. We investigate when a product's visual separation from its closest look-alike in a returned set increases its choice probability. We introduce two occasion-level constructs: contextual visual distinctiveness (image distance from the nearest similar alternative in the consideration set) and relative fit (compatibility with the search query). We hypothesize that distinctiveness favors selection and is highly contextual, yields a premium that rises with relative fit, and matters most when text descriptions fail to differentiate options. Analyzing over 800,000 e-commerce search events using dense representations, we apply search-event and product fixed effects to evaluate the exact same product alongside varying visual neighbors. Results show a product is significantly more likely to be clicked when it lacks a close look-alike. This distinctiveness premium increases with relative fit and roughly doubles when competing descriptions are highly similar. Consistent with a model where distinctiveness aids in standing out pre-evaluation rather than increasing inherent utility, the extra clicks distinctiveness recruits convert 5-7% less often downstream. Ultimately, the evidence characterizes visual differentiation as a local attention allocation mechanism whose value depends on query fit and information from competing cues.
