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2013 | OriginalPaper | Chapter

Logic Based Conjoint Analysis Using the Commuting Quantum Query Language

Authors : Ingo Schmitt, Daniel Baier

Published in: Algorithms from and for Nature and Life

Publisher: Springer International Publishing

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Abstract

Recently, in computer science, the commuting quantum query language (CQQL) has been introduced for ranking search results in a database (Schmitt I, VLDB Very Large Data Base J 17(1):39–56, 2008). A user is asked to express search conditions and to rank presented samples, then, the CQQL system matches select-conditions with attribute values. Schmitt’s (VLDB Very Large Data Base J 17(1):39–56, 2008) approach is based on von Neumann’s (Grundlagen der Quantenmechanik. Springer, Berlin, 1932) quantum mechanics and quantum logic and is adapted in this paper to the usual conjoint analysis setting in marketing research where respondents are asked to evaluate selected attribute level combinations for choice prediction purposes. The results of an application show that the new approach competes well with the traditional approach.

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Footnotes
1
1.0 corresponds to true and 0.0 to false.
 
2
For simplicity, we assume metric attribute values. The corresponding proximity predicates compare the value closeness to a highest or a lowest value. Due to space restriction, we do not discuss here the non-metric case.
 
3
Recall, that the rules of Boolean algebra hold.
 
4
For simplicity, the expression \([a\mid b]\) means either a or b. This decision depends on the corresponding minterm.
 
5
Please notice, that two different minterms differ always in the negation of at least one predicate. Therefore, disjunction of minterms is mapped to a simple sum.
 
Literature
go back to reference Baier, D., & Brusch, M. (Eds.) (2009). Conjointanalyse: Methoden – Anwendungen – Praxisbeispiele. Berlin: Springer. Baier, D., & Brusch, M. (Eds.) (2009). Conjointanalyse: Methoden – Anwendungen – Praxisbeispiele. Berlin: Springer.
go back to reference Fürnkranz, J., & Hüllermeier, E. (Eds.) (2010). Preference learning. Berlin: Springer.MATH Fürnkranz, J., & Hüllermeier, E. (Eds.) (2010). Preference learning. Berlin: Springer.MATH
go back to reference Green, P. E., & RAO, V. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8(3), 355–363. Green, P. E., & RAO, V. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8(3), 355–363.
go back to reference Saretz, S. (2010). Evaluierung und Abgrenzung der Ähnlichkeitsanfragesprache CQQL gegenüber anderen DB-Retrieval-Verfahren. Working paper series, Institute of Computer Science, Information and Media Technology, BTU Cottbus. Saretz, S. (2010). Evaluierung und Abgrenzung der Ähnlichkeitsanfragesprache CQQL gegenüber anderen DB-Retrieval-Verfahren. Working paper series, Institute of Computer Science, Information and Media Technology, BTU Cottbus.
go back to reference Schmitt, I. (2008). QQL: A DB&IR query language. The VLDB (Very Large Data Base) Journal, 17(1), 39–56.CrossRef Schmitt, I. (2008). QQL: A DB&IR query language. The VLDB (Very Large Data Base) Journal, 17(1), 39–56.CrossRef
Metadata
Title
Logic Based Conjoint Analysis Using the Commuting Quantum Query Language
Authors
Ingo Schmitt
Daniel Baier
Copyright Year
2013
DOI
https://doi.org/10.1007/978-3-319-00035-0_49

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