Title
Modeling Relevance Judgement Inspired by Quantum Weak Measurement.
Abstract
Concept in Quantum theory (QT) has been successfully inspired analogous concepts in the field of Information Retrieval (IR). Many IR researchers have employed the QT to investigate cognitive phenomena within user behaviors, and also have verified the existence of quantum-like phenomena in real web search. However, for some complex search task, in which user's information need (IN) is dynamic and hard to be captured, QT currently adopted still can not explain some more complex cognitive phenomena. In this paper, a user experiment is conducted to investigate the variance of relevance judgement, and its results demonstrate that quantumWeak Measurement (WM) is more appropriate than the standard quantum measurement to model relevance judgement. Further, a WM-based session search model (WSM) is presented to model user's dynamic evolving IN. The extensive experiments are tested on the session track of TREC 2013 & 2014 and verify the effectiveness of WSM.
Year
DOI
Venue
2018
10.1007/978-3-319-76941-7_32
ADVANCES IN INFORMATION RETRIEVAL (ECIR 2018)
Keywords
Field
DocType
Relevance judgement,Quantum weak measurement,Session search
Quantum,Information needs,Information retrieval,Computer science,Judgement,Quantum measurement,Session search,Cognition,Weak measurement
Conference
Volume
ISSN
Citations 
10772
0302-9743
0
PageRank 
References 
Authors
0.34
14
4
Name
Order
Citations
PageRank
Panpan Wang1205.75
Tianshu Wang243.43
Yuexian Hou326938.59
Dawei Song443641.48