Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten suggestions per search query pose the most significant problem in identifying bias in search query suggestions. The most proven method to reduce sparseness and improve the validity of bias identification of search query suggestions so far is to consider suggestions from subsequent searches over time for the same query. This work presents a new, alternative approach to search query bias identification that includes less high-level suggestions to deepen the data basis of bias analyses. We employ recursive algorithm interrogation techniques and create suggestion trees that enable access to more subliminal search query suggestions. Based on these suggestions, we investigate topical group bias in person-related searches in the political domain.

翻译：尽管搜索查询建议在在线信息搜索中扮演着重要角色，但其研究程度远不及搜索引擎的其他大多数方面。造成这一现象的原因是多方面的，其中上下文稀疏性以及每个搜索查询最多十条建议的有限数据基础，构成了识别搜索查询建议偏见的最显著障碍。迄今为止，减少稀疏性并提升搜索查询建议偏见识别有效性的最成熟方法，是考虑同一查询随时间推移在后续搜索中产生的建议。本研究提出了一种全新的替代性搜索查询偏见识别方法，该方法通过纳入更多低层级建议来深化偏见分析的数据基础。我们采用递归算法质询技术，构建能够访问更多潜意识层面搜索查询建议的“建议树”。基于这些建议，我们针对政治领域人物相关搜索中的主题群体偏见进行了实证研究。