Exploring users' perception of collaborative explanation styles

Ludovik Coba, Markus Zanker, Laurens Rook, Panagiotis Symeonidis

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)


Collaborative 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.

Original languageEnglish
Title of host publicationResearch Papers
EditorsH.A. Proper , S. Strecker, C. Huemer
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Print)978-153867016-3
Publication statusPublished - 2018
Event20th IEEE International Conference on Business Informatics, CBI 2018 - Vienna, Austria
Duration: 11 Jul 201813 Jul 2018


Conference20th IEEE International Conference on Business Informatics, CBI 2018


  • Collaborative filtering
  • Conjoint experiment
  • Explanations
  • Recommender systems


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