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.
|Number of pages||16|
|Journal||International Journal of Machine Learning and Cybernetics|
|Early online date||3 Oct 2016|
|Publication status||Published - May 2018|
Bibliographical noteThe work described in this paper was fully supported by the Start-Up Research Grant (RG 37/2016-2017R) of The Education University of Hong Kong, a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14), Hong Kong Research Grants Council under Project ECS/HKBU 211912, Internal Research Grant of the Hong Kong Institute of Education (RG 30/2014-2015), China National Natural Science Foundation under Project NSFC/61272365 and 61502545, Soft Science Research Project of Guangdong Province (2014A030304013), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase), and the Fundamental Research Funds for the Central Universities under Project 46000-31610009. The preliminary version of this paper was published at UMAP 2014.
- Collaborative approach
- Compound critiquing
- Conversational recommender
- Retrospective user study