Characterizing and Early Predicting User Performance for Adaptive Search Path Recommendation

Abstract

ABSTRACT User search performance is multidimensional in nature and may be better characterized by metrics that depict users' interactions with both relevant and irrelevant results. Despite previous research on one‐dimensional measures, it is still unclear how to characterize different dimensions of user performance and leverage the knowledge in developing proactive recommendations. To address this gap, we propose and empirically test a framework of search performance evaluation and build early performance prediction models to simulate proactive search path recommendations. Experimental results from four datasets of diverse types (1,482 sessions and 5,140 query segments from both controlled lab and natural settings) demonstrate that: 1) Cluster patterns characterized by cost‐gain‐based multifaceted metrics can effectively differentiate high‐performing users from other searchers, which form the empirical basis for proactive recommendations; 2) whole‐session performance can be reliably predicted at early stages of sessions (e.g., first and second queries); 3) recommendations built upon the search paths of system‐identified high‐performing searchers can significantly improve the search performance of struggling users. Experimental results demonstrate the potential of our approach for leveraging collective wisdom from automatically identified high‐performance user groups in developing and evaluating proactive in‐situ search recommendations.