Automata modeling for cognitive interference in users' relevance judgement
Zhang, Peng, Song, Dawei, Hou, Yuexian, Wang, Jun, & Bruza, Peter D. (2010) Automata modeling for cognitive interference in users' relevance judgement. In Bruza, Peter D., Lawless, William, van Rijsbergen, Keith, Sofge, Donald A, & Widdows, Dominic (Eds.) Proceedings of the AAAI Fall Symposium on Quantum Informatics for Cognitive, Social and Semantic Processes 2010, AAAI Press, USA, pp. 125-133.
Quantum theory has recently been employed to further advance the theory of information retrieval (IR). A challenging research topic is to investigate the so called quantum-like interference in users’ relevance judgement process, where users are involved to judge the relevance degree of each document with respect to a given query. In this process, users’ relevance judgement for the current document is often interfered by the judgement for previous documents, due to the interference on users’ cognitive status.
Research from cognitive science has demonstrated some initial evidence of quantum-like cognitive interference in human decision making, which underpins the user’s relevance judgement process. This motivates us to model such cognitive interference in the relevance judgement process, which in our belief will lead to a better modeling and explanation of user behaviors in relevance judgement process for IR and eventually lead to more user-centric IR models.
In this paper, we propose to use probabilistic automaton(PA) and quantum finite automaton (QFA), which are suitable to represent the transition of user judgement states, to dynamically model the cognitive interference when the user is judging a list of documents.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Quantum theory, Information retrieval|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved.|
|Deposited On:||31 Jan 2012 08:13|
|Last Modified:||01 Mar 2012 10:27|
Repository Staff Only: item control page