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Serendipity in the city: User evaluations of urban recommender systems
Journal of the Association for Information Science and Technology ( IF 3.5 ) Pub Date : 2021-07-21 , DOI: 10.1002/asi.24552
Annelien Smets 1 , Jorre Vannieuwenhuyze 1 , Pieter Ballon 1
Affiliation  

The contemporary city is increasingly being labeled as a smart city consisting of both physical and virtual spaces. This digital augmentation of urban life sets the scene for urban recommender systems to help citizens dealing with the abundance of digital information and corresponding choice overload, for example, by recommending the best place to have dinner based on your personal profile. There are, however, concerns that this kind of algorithmic filtering could lead to homogenization of urban experiences and a decline of social cohesion among citizens. To overcome this issue, scholars increasingly encourage the introduction of serendipity in all types of recommender systems. Nonetheless, it remains unclear how this can be achieved in practice. In this work, we study user evaluations of serendipity in urban recommender systems through a survey among 1,641 citizens. More specifically, we study which characteristics of recommended items contribute to serendipitous experiences and to what extent this increases user satisfaction and conversion. Our results align with findings in other application domains in the sense that there is a strong relation between the relevance and novelty of recommendations and the corresponding experienced serendipity. Moreover, serendipitous recommendations are found to increase the chance of users following up on these recommendations.

中文翻译:

城市中的意外:城市推荐系统的用户评价

当代城市越来越多地被贴上由物理和虚拟空间组成的智慧城市的标签。城市生活的这种数字增强为城市推荐系统奠定了基础,以帮助市民处理大量数字信息和相应的选择过载,例如,根据您的个人资料推荐最佳用餐地点。然而,有人担心这种算法过滤可能导致城市体验的同质化和公民之间社会凝聚力的下降。为了克服这个问题,学者们越来越鼓励在所有类型的推荐系统中引入偶然性。尽管如此,目前尚不清楚如何在实践中实现这一目标。在这项工作中,我们通过对 1,641 名市民的调查研究了城市推荐系统中用户对意外的评价。更具体地说,我们研究推荐项目的哪些特征有助于偶然体验,以及这在多大程度上提高了用户满意度和转化率。我们的结果与其他应用领域的发现一致,因为推荐的相关性和新颖性与相应的经验性偶然性之间存在很强的关系。此外,发现偶然的推荐可以增加用户跟进这些推荐的机会。我们的结果与其他应用领域的发现一致,因为推荐的相关性和新颖性与相应的经验性偶然性之间存在很强的关系。此外,发现偶然的推荐可以增加用户跟进这些推荐的机会。我们的结果与其他应用领域的发现一致,因为推荐的相关性和新颖性与相应的经验性偶然性之间存在很强的关系。此外,发现偶然的推荐可以增加用户跟进这些推荐的机会。
更新日期:2021-07-21
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