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

TL;DR

This study compares user preferences for Linear and Directional Top-k Queries across different contexts, revealing that preferences vary based on domain and user familiarity, with implications for personalized ranking systems.

Contribution

It provides empirical insights into how user preferences differ for ranking methods in real-world scenarios, emphasizing the importance of context and domain in query design.

Findings

Directional queries preferred in used cars domain

Preferences vary with user expertise in football domain

Significant differences in preferences across topics

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.