Understanding Visual Saliency of Outlier Items in Product Search

• URL: http://arxiv.org/abs/2503.23596v1
• Date: Sun, 30 Mar 2025 21:22:23 GMT
• Title: Understanding Visual Saliency of Outlier Items in Product Search
• Authors: Fatemeh Sarvi, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke,
• Abstract summary: In two-sided marketplaces, items compete for user attention, which translates to revenue for suppliers.<n>Recent work suggests that inter-item dependencies, such as outlier items in a ranking, also affect item exposure.<n>We investigate how top-down factors influence users' perception of item outlierness in a realistic online shopping scenario.
• Score: 62.12411635661447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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