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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 5/2018

03-10-2016 | Original Article

Incorporating user experience into critiquing-based recommender systems: a collaborative approach based on compound critiquing

Authors: Haoran Xie, Debby D. Wang, Yanghui Rao, Tak-Lam Wong, Lau Y. K. Raymond, Li Chen, Fu Lee Wang

Published in: International Journal of Machine Learning and Cybernetics | Issue 5/2018

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Abstract

Critiques are employed as user feedback in critiquing-based recommender systems and they play an important role in the learning of user preferences, where recommender systems can gradually refine their understanding of user needs and provide better recommendations to users in subsequent interaction sessions. To reduce the effort of user interaction, the advantage of improving the recommendation efficiency by exploring relevant critiquing sessions in the interaction histories of other users has been recognized in recent studies of experience-based critiquing. In this study, we propose a novel approach for processing the historical interaction data in compound critiquing systems. In particular, we describe a history-aware collaborative compound critiquing method, which combines the strategies of preference-based compound critiquing generation and graph-based relevant session identification. Based on a simulation study using real-life data sets, we demonstrated that the proposed method outperformed other experience-based critiquing methods in terms of the recommendation efficiency. We also conducted a retrospective user evaluation, which confirmed the following observations: (1) incorporating user experience into compound critiquing systems significantly improves the performance compared with traditional unit critiquing systems; and (2) our graph-based session identification approach is superior to other baseline methods in terms of reducing the interaction effort of users.
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Footnotes
1
\(=,>,<\) are used for numerical attributes, and \(=,\ne\) are for categorical attributes.
 
2
Note that the three methods (HIGH, LOW, and RAND) have a tuned threshold (\(70\,\%\)) for the support value.
 
3
\(|I^{'}|\) and \(|I^{*}|\) are the numbers of items in \(s_{i}\) and \(s_{j}\), respectively (the finally accepted item is excluded).
 
4
\(OverlapScore(s_{i},s_{j}) = [\sum _{c^{'}\in s_{i}}\sum _{c^{*}\in s_{j}}match({c^{'},c^{*}})]^{2}\); if \(c^{'} = c^{*}\), \(match() = 1\); otherwise, \(match() = 0\).
 
5
\(Compatibility(i_{t},q)\) is the number of satisfied critiques in the current session q that are satisfied by the item \(i_{t}\) (i.e., \(Compatibility(i_{t},q) = |\{c_{i}|satisfies(i_{t},c_{i}),c_{i}\in q\}|).\)
 
6
\(Size_{base}\) was set as 836 and 4898 for the CAR and WINE data sets, respectively.
 
7
The p value was calculated using the Student’s t test.
 
8
\(Size_{base}=863\) for the LAPTOP data set, and 406 and 4898 for the CAR and WINE data sets, respectively.
 
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Metadata
Title
Incorporating user experience into critiquing-based recommender systems: a collaborative approach based on compound critiquing
Authors
Haoran Xie
Debby D. Wang
Yanghui Rao
Tak-Lam Wong
Lau Y. K. Raymond
Li Chen
Fu Lee Wang
Publication date
03-10-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 5/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0611-2

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