理解产品搜索中离群项目的视觉显著性
Understanding Visual Saliency of Outlier Items in Product Search
AI总结：
本研究通过视觉显著性模型和眼动追踪实验，探究产品搜索中离群项目的视觉显著性，发现自下而上视觉因素和自上而下因素共同影响用户感知，离群项目能延长用户参与时间。
摘要：
在双边市场中，项目竞争用户的注意力，这转化为供应商的收入。项目曝光度，即项目在排名中获得的注意力数量，可能受到位置偏差等因素的影响。近期研究表明，项目间的相互依赖关系，如排名中的离群项目，也会影响项目曝光度。离群项目是指在排名列表中明显偏离其他项目的项目。理解离群项目对于确定项目的曝光分布至关重要。在我们先前的工作中，我们调查了不同呈现特征对用户在搜索结果中感知离群的影响。在本工作中，我们聚焦于先前工作遗留的两个关键问题：（i）孤立的自下而上视觉因素对产品列表中项目离群性的影响是什么？（ii）在现实在线购物场景中，自上而下因素如何影响用户对项目离群性的感知？我们从自下而上因素开始，采用视觉显著性模型来评估它们仅基于视觉属性检测产品列表中离群项目的能力。然后，为了考察自上而下因素，我们在在线购物任务中进行了眼动追踪实验。此外，我们使用眼动追踪不仅为了更接近真实世界情况，还为了解决视觉搜索任务中反应时间的准确性问题。我们的实验展示了视觉显著性模型检测自下而上因素的能力，一致地突出具有强烈视觉对比的区域。针对无离群项目列表的眼动追踪实验结果表明，尽管产品描述在视觉上吸引力较低，但能最快捕获注意力，表明自上而下因素的重要性。在我们的眼动追踪实验中，我们观察到离群项目相比非离群项目，使用户参与时间更长。
英文摘要：
In two-sided marketplaces, items compete for user attention, which translates to revenue for suppliers. Item exposure, indicated by the amount of attention items receive in a ranking, can be influenced by factors like position bias. Recent work suggests that inter-item dependencies, such as outlier items in a ranking, also affect item exposure. Outlier items are items that observably deviate from the other items in a ranked list. Understanding outlier items is crucial for determining an item's exposure distribution. In our previous work, we investigated the impact of different presentational features on users' perception of outlier in search results. In this work, we focus on two key questions left unanswered by our previous work: (i) What is the effect of isolated bottom-up visual factors on item outlierness in product lists? (ii) How do top-down factors influence users' perception of item outlierness in a realistic online shopping scenario? We start with bottom-up factors and employ visual saliency models to evaluate their ability to detect outlier items in product lists purely based on visual attributes. Then, to examine top-down factors, we conduct eye-tracking experiments on an online shopping task. Moreover, we employ eye-tracking to not only be closer to the real-world case but also to address the accuracy problem of reaction time in the visual search task. Our experiments show the ability of visual saliency models to detect bottom-up factors, consistently highlighting areas with strong visual contrasts. The results of our eye-tracking experiment for lists without outliers show that despite being less visually attractive, product descriptions captured attention the fastest, indicating the importance of top-down factors. In our eye-tracking experiments, we observed that outlier items engaged users for longer durations compared to non-outlier items.
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