Understanding User Preference -- Comparison between Linear and Directional Top-K Query results

Abstract: This paper investigates user preferences for Linear Top-k Queries and Directional Top-k Queries, two methods for ranking results in multidimensional datasets. While Linear Queries prioritize weighted sums of attributes, Directional Queries aim to deliver more balanced results by incorporating the spatial relationship between data points and a user-defined preference line. The study explores how preferences for these methods vary across different contexts by focusing on two real-world topics: used cars (e-commerce domain) and football players (personal interest domain). A user survey involving 106 participants was conducted to evaluate preferences, with results visualized as scatter plots for comparison. The findings reveal a significant preference for directional queries in the used cars topic, where balanced results align better with user goals. In contrast, preferences in the football players topic were more evenly distributed, influenced by user expertise and familiarity with the domain. Additionally, the study demonstrates that the two specific topics selected for this research exhibit significant differences in their impact on user preferences. This research reveals authentic user preferences, highlighting the practical utility of Directional Queries for lifestyle-related applications and the subjective nature of preferences in specialized domains. These insights contribute to advancing personalized database technologies, guiding the development of more user-centric ranking systems.