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 w.r.t. task-specific, presentational features. 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 outlierness in e-commerce search result pages. By modeling the problem as visual search tasks, we compared the observability of three main features: price, star rating, and discount tag. We found that participants perceive these features differently in terms of attention and reaction times. Various factors, such as visual complexity (e.g., shape, color), discriminative item features (e.g., a solitary discount tag), and value range, affect item outlierness. These factors can be categorized into two main classes: bottom-up and top-down . Bottom-up factors are driven by visual properties such as color, contrast, and brightness, while top-down factors are influenced by cognitive processes such as expectations and prior knowledge. In this extension of our previous work, we deepen our analysis of user perceptions of outliers. In particular, 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 the same task as our previous visual search experiment: online shopping. This time, we design the task as a simulated e-commerce environment, mimicking a popular European online shopping platform to be more representative of real-world scenarios. 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. In our experiments, participants interact with realistic product lists, some containing outliers w.r.t. different presentational features, such as image, price, and discount tag, at different positions. Our experiments show the ability of visual saliency models to detect bottom-up factors, consistently highlighting areas with strong visual contrasts and attention hotspots. While the well-known Itti and Koch model detects general visual attention patterns in an image, a graph-based visual saliency (GBVS) model identifies visual anomalies more effectively. However, one should be cautious about the limitations of these models. Visual saliency models only rely on bottom-up factors, making them naive in that they do not distinguish between separate product features or compare them against each other. 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 and user knowledge of the task. Our observations in lists with visual outliers suggest that outliers and their immediate neighbors attracted attention faster (in terms of time to first fixation), which is in line with our findings from the visual search task. However, in our eye-tracking experiments, we observed that outlier items engaged users for longer durations (in terms of fixation count and time spent) compared to non-outlier items. This effect was consistent across different outlier features (image, price, discount tag) and various positions within the list.

In two-sided marketplaces, items compete for attention from users since attention translates to revenue for suppliers. Item exposure is an indication of the amount of attention that items receive from users in a ranking. It can be influenced by factors like position bias. Recent work suggests that another phenomenon related to inter-item dependencies may also affect item exposure, viz. outlier items in the ranking. Hence, a deeper understanding of outlier items is crucial to determining an item’s exposure distribution. In this work, we study the impact of different presentational e-commerce features on users’ perception of outlierness of an item in a search result page. Informed by visual search literature, we design a set of crowdsourcing tasks where we compare the observability of three main features, viz. price, star rating, and discount tag. We find that various factors affect item outlierness, namely, visual complexity (e.g., shape, color), discriminative item features, and value range. In particular, we observe that a distinctive visual feature such as a colored discount tag can attract users’ attention much easier than a high price difference, simply because of visual characteristics that are easier to spot. Moreover, we see that the magnitude of deviations in all features affects the task complexity, such that when the similarity between outlier and non-outlier items increases, the task becomes more difficult.

User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to rank (ULTR) focuses on position bias, i.e., items at higher ranks are more likely to be examined and clicked. Inter-item dependencies also influence examination probabilities, with outlier items in a ranking as an important example. They are defined as items that observably deviate from the rest and therefore stand out in the ranking. In this paper, we identify and introduce the bias brought about by outlier items: users tend to click more on outlier items and their close neighbors.

Implicit feedback is an attractive source of training data in Learning to Rank (LTR). However, naively use of this data can produce unfair ranking policies originating from both exogenous and endogenous factors. Exogenous factors comes from biases in the training data, which can lead to rich-get-richer dynamics. Endogenous factors can result in ranking policies that do not allocate exposure among items in a fair way. Item exposure is a common components influencing both endogenous and exogenous factors which depends on not only position but also Inter-item dependencies. In this project, we focus on a specific case of these Inter-item dependencies which is the existence of an outlier in the list. We first define and formalize outlierness in ranking, then study the effects of this phenomenon on endogenous and exogenous factors. We further investigate the visual aspects of presentational features and their impact on item outlierness.

Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based on the idea that all items or item groups should get exposure proportional to the merit of the item or the collective merit of the items in the group. Often, stochastic ranking policies are used to ensure fairness of exposure. Previous work unrealistically assumes that we can reliably estimate the expected exposure for all items in each ranking produced by the stochastic policy. In this work, we discuss how to approach fairness of exposure in cases where the policy contains rankings of which, due to inter-item dependencies, we cannot reliably estimate the exposure distribution. In such cases, we cannot determine whether the policy can be considered fair. % Our contributions in this paper are twofold. First, we define a method called \method for finding stochastic policies that avoid showing rankings with unknown exposure distribution to the user without having to compromise user utility or item fairness. Second, we extend the study of fairness of exposure to the top-k setting and also assess \method in this setting. We find that \method can significantly reduce the number of rankings with unknown exposure distribution without a drop in user utility or fairness compared to existing fair ranking methods, both for full-length and top-k rankings. This is an important first step in developing fair ranking methods for cases where we have incomplete knowledge about the user's behaviour.

There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. \acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, \acPL optimization has room for improvement for queries with a small number of repeating sessions.

Traditional ranking systems are expected to sort items in the order of their relevance and thereby maximize their utility. In fair ranking, utility is complemented with fairness as an optimization goal. Recent work on fair ranking focuses on developing algorithms to optimize for fairness, given position-based exposure. In contrast, we identify the potential of outliers in a ranking to influence exposure and thereby negatively impact fairness. An outlier in a list of items can alter the examination probabilities, which can lead to different distributions of attention, compared to position-based exposure. We formalize outlierness in a ranking, show that outliers are present in realistic datasets, and present the results of an eye-tracking study, showing that users scanning order and the exposure of items are influenced by the presence of outliers. We then introduce OMIT, a method for fair ranking in the presence of outliers. Given an outlier detection method, OMIT improves fair allocation of exposure by suppressing outliers in the top-k ranking. Using an academic search dataset, we show that outlierness optimization leads to a fairer policy that displays fewer outliers in the top-k, while maintaining a reasonable trade-off between fairness and utility.