Exploring users' perception of rating summary statistics

Ludovik Coba, Markus Zanker, Laurens Rook, Panagiotis Symeonidis

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

2 Citations (Scopus)
98 Downloads (Pure)


Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. These summary statistics of rating values carry two important descriptors about the assessed items, namely the total number of ratings and the mean rating value. In this study we explore how these two signals influence the decisions of online users based on choice-based conjoint experiments. Results show that users are more inclined to follow the mean indicator as opposed to the total number of ratings. 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 it explainability due to these ratings when ranking recommendations.
Original languageEnglish
Title of host publicationProceedings of the 26th Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery (ACM)
Number of pages354
Publication statusPublished - 2018


  • explanation styles
  • recommender systems
  • user studies


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