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dc.contributor.authorCoba L
dc.contributor.authorZanker M
dc.contributor.authorRook L
dc.contributor.authorSymeonidis P
dc.contributor.editor
dc.date.accessioned2019-02-14T16:20:56Z
dc.date.available2019-02-14T16:20:56Z
dc.date.issued2018
dc.identifier.isbn978-1-5386-7017-0
dc.identifier.urihttp://dx.doi.org/10.1109/CBI.2018.00017
dc.identifier.urihttps://ieeexplore.ieee.org/document/8452660
dc.identifier.urihttp://hdl.handle.net/10863/8217
dc.description.abstractCollaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-based paradigm. Furthermore, we explore how the characteristics of these rating summarizations, like the total number of ratings and the mean rating value, influence the decisions of online users. Results, based on a choice-based conjoint experimental design, show that the mean indicator has a higher impact compared to the total number of ratings. Finally, we discuss how these empirical results can serve as an input to developing algorithms that foster items with a, consequently, higher probability of choice based on their rating summarizations or their explainability due to these ratings when ranking recommendations.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation20th IEEE Conference on Business Informatics ; Vienna : 11.7.2018 - 14.7.2018
dc.rights
dc.titleExploring Users' Perception of Collaborative Explanation Stylesen_US
dc.typeBook chapteren_US
dc.date.updated2019-02-12T09:16:24Z
dc.publication.title2018 20th IEEE International Conference on Business Informatics: 11-13 July 2018, Vienna, Austria : proceedings
dc.language.isiEN-GB
dc.description.fulltextopenen_US


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