Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/230
DC FieldValueLanguage
dc.contributor.authorXie, Haoran-
dc.contributor.authorWang, Debby Dan-
dc.contributor.authorWang, Philips Fu Lee-
dc.contributor.authorWong, Tak Lam-
dc.contributor.otherRao, Y.-
dc.contributor.otherRaymond, L. Y. K.-
dc.contributor.otherChen, L.-
dc.date.accessioned2021-03-16T06:41:46Z-
dc.date.available2021-03-16T06:41:46Z-
dc.date.issued2018-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/230-
dc.description.abstractCritiques 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.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInternational Journal of Machine Learning and Cyberneticsen_US
dc.titleIncorporating user experience into critiquing-based recommender systems: A collaborative approach based on compound critiquingen_US
dc.typejournal articleen_US
dc.identifier.doi10.1007/s13042-016-0611-2-
dc.contributor.affiliationSchool of Computing and Information Sciences-
dc.relation.issn1868-808Xen_US
dc.description.volume9en_US
dc.description.issue5en_US
dc.description.startpage837en_US
dc.description.endpage852en_US
dc.cihe.affiliatedYes-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.openairetypejournal article-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.deptRita Tong Liu School of Business and Hospitality Management-
crisitem.author.deptSchool of Computing and Information Sciences-
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