The proliferation of smartphones has led to the increased popularity of location-based search and recommendation systems. Online platforms like Google and Yelp allow location-based search in the form of nearby feature to query for hotels or restaurants in the vicinity. Moreover, hotel booking platforms like Booking[dot]com, Expedia, or Trivago allow travelers searching for accommodations using either their desired location as a search query or near a particular landmark. Since the popularity of different locations in a city varies, certain locations may get more queries than other locations. Thus, the exposure received by different establishments at these locations may be very different from their intrinsic quality as captured in their ratings.
Today, many small businesses (shops, hotels, or restaurants) rely on such online platforms for attracting customers. Thus, receiving less exposure than that is expected can be unfavorable for businesses. It could have a negative impact on their revenue and potentially lead to economic starvation or even shutdown. By gathering and analyzing data from three popular platforms, we observe that many top-rated hotels and restaurants get less exposure vis-a-vis their quality, which could be detrimental for them. Following a meritocratic notion, we define and quantify such exposure disparity due to location-based searches on these platforms. We attribute this exposure disparity mainly to two kinds of biases -- Popularity Bias and Position Bias. Our experimental evaluation on multiple datasets reveals that although the platforms are doing well in delivering distance-based results, exposure disparity exists for individual businesses and needs to be reduced for business sustainability.
Evaluating Stochastic Rankings with Expected Exposure
我们引入了\emph{expected exposure}的概念，作为平均关注度排名项目收到来自用户的重复样本相同的查询。此外，我们主张采用平等的预期曝光原则：给定一个固定的信息需求，没有一个项目的预期曝光应该比相同相关性等级的任何其他项目更多或更少。我们认为，这一原则是可取的许多检索目标和场景，包括主题多样性和公平的排名。利用现有的检索指标的用户模型，我们提出了一个通用的评估方法，基于预期的曝光和绘制连接到相关的指标在信息检索评估。重要的是，这种方法放松了经典的信息检索假设，允许系统响应于查询产生一个\emph{distribution over rankings}，而不是一个单一的固定排名。我们研究的行为，预期的曝光度量和随机排名在各种信息访问条件，包括\emph{ad hoc}检索和推荐。我们相信，使用随机化来测量和优化预期的暴露度量为检索算法的开发和进步开辟了一个新的领域。
University of Science and Technology of China（中国科学技术大学）；University of Electronic Science and Technology of China（电子科技大学）；Lanzhou University（兰州大学）；University of Glasgow（格拉斯哥大学）；City University of Hong Kong（香港城市大学）；The Ohio State University（俄亥俄州立大学）
Diagnosing LLM-based Rerankers in Cold-Start Recommender Systems: Coverage, Exposure and Practical Mitigations
V. A. Trapeznikov ICS RAS Russian Biotechnological University (ROSBIOTECH)（Trapeznikov ICS 俄罗斯科学院俄罗斯生物技术大学）；Russian Biotechnological University (ROSBIOTECH)（俄罗斯生物技术大学）
Exposing Query Identification for Search Transparency
Carnegie Mellon University（卡内基梅隆大学）；Microsoft（微软）；University College London（伦敦大学学院）；Mila Quebec Artificial Intelligence Institute（米拉魁北克人工智能研究所）；Max Planck Institute for Security and Privacy（马克斯·普朗克安全与隐私研究所）
