Incorporating user experience into critiquing-based recommender systems: A collaborative approach based on compound critiquing
Author(s)
Xie, Haoran
Wang, Debby Dan
Wang, Philips Fu Lee
Wong, Tak Lam
Author(s)
Rao, Y.
Raymond, L. Y. K.
Chen, L.
Date Issued
2018
Publisher
Springer
Journal
International Journal of Machine Learning and Cybernetics
Volume
9
Issue
5
Start page
837
End page
852
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.
SFU Affiliated Publication
Yes
Availability at SFU Library
No database links found.